Computing system implementing morbidity prediction using a correlative health assertion library

ABSTRACT

A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a morbidity risk profile that is used to classify the user in an underwriting class.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/784,641, filed on Feb. 7, 2020; which is a continuation of U.S.patent application Ser. No. 15/273,618, filed on Sep. 22, 2016, now U.S.Pat. No. 10,636,525; which is a continuation-in-part of U.S. patentapplication Ser. No. 14/642,709, filed Mar. 9, 2015, now U.S. Pat. No.10,629,293; which is a continuation-in-part of U.S. patent applicationSer. No. 14/542,347, filed Nov. 14, 2014, now U.S. Pat. No. 10,672,519;the aforementioned applications being hereby incorporated by referencein their respective entireties.

TECHNICAL FIELD

Examples described herein relate to a system and method for providing ahealth determination service based on user knowledge and activity.

BACKGROUND

Online services exist which provide interactive gaming and socialenvironments for users. These services generally exist for amusementonly.

There also exists a questionnaire, termed the Patient Activation Measure(“PAM”), provided by Insignia Health under license from the State ofOregon, which includes a static set of questions that areknowledge-based and deemed correlative to health.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for predicting a physiological or mentalhealth of a user based on the user's knowledge level of health,according to one or more embodiments.

FIG. 2 illustrates an analysis system, according to an embodiment.

FIG. 3 illustrates an example of a data structure that can be developedto link a question with a health outcome and a topic, according to oneor more embodiments.

FIG. 4 illustrates an example method for predicting a health outcome ofa user based in part on whether a user has independent knowledge of anassertion relating to health.

FIG. 5 illustrates an example method for predicting a health outcome ofa user based on a knowledge profile of a user.

FIG. 6A illustrates an example method for providing a health relatedservice to a user based on a knowledge-predicted health outcome for auser.

FIG. 6B illustrates a health service sub-system 680, according to anembodiment.

FIG. 7A illustrates an example method for providing a game-basedenvironment in which user responses enable prediction of health outcomesfor individual users.

FIG. 7B illustrates a knowledge-based recommendation engine, accordingto one or more embodiments.

FIG. 7C illustrates an example method for choosing questions to provideto a user based on data retrieved from an activity monitoring device.

FIG. 8A through 8H illustrate example interfaces for use with one ormore embodiments described herein.

FIG. 9 is a block diagram that illustrates a computer system upon whichembodiments described herein may be implemented.

FIG. 10 illustrates a real-world mortality information system, accordingto an embodiment.

FIG. 11 illustrates a system to categorize a lifestyle of a user,according to an embodiment.

FIG. 12 illustrates an automated method for utilizing information abouta user, according to an embodiment.

FIG. 13 illustrates a health determination system for determininghealth-based products and services, according to an embodiment.

DETAILED DESCRIPTION

Some embodiments include a system and method for predicting a healthoutcome of a user based on a determination of knowledge the userpossesses regarding issues of physiological or mental health.

Still further, in some embodiments, a system and method is provided forproviding a health service benefit to a user based on their predictedhealth, as determined from the user's knowledge of human health.

In one embodiment, a collection of assertions are stored in which eachassertion pertains to human health. For each individual in a controlpopulation of persons, a value of a predetermined health parameter isdetermined which is indicative of that person's health. For eachassertion of the collection, a correlative health parameter isdetermined which is indicative of an association between thoseindividuals in the control population that have independent knowledge ofthe assertion and the value of the predetermined health parameter forpersons of the control population. The collection of assertions can bestored by associating each assertion with the determined correlativehealth parameter for that assertion. An interface is provided for a userto indicate the user's independent knowledge of each assertion in atleast a subset of assertions from the collection. A health outcome ispredicted for the user based at least in part on the correlative healthparameter of individual assertions in the subset of assertions.

In still another embodiment, a health outcome of a user is predictedbased on a knowledge profile determination of the user. In oneembodiment, a knowledge profile is determined for the user whichreflects the user's independent knowledge of individual assertions in acollection of assertions. A correlation is determined as between a setof facets of the user's knowledge profile and a corresponding set offacets of multiple individual person's knowledge profile. The knowledgeprofile can be determined for at least a set of assertions from thecollection of assertions. A health outcome is determined for each of themultiple individual persons. The health outcome of the user can then bepredicted based in part on the correlation and the health outcome ofeach of the multiple individuals.

In still another embodiment, a knowledge profile is determined for theuser to reflect the user's independent knowledge of individualassertions in a collection of assertions. Each assertion in thecollection can be non-specific to the user or to any person of thepopulation, but otherwise known to be correlative to human health. Adetermination is made as to a first correlation value as between theknowledge profile of the user and a knowledge profile of a control groupof persons for whom one or more health outcomes are known. A firsthealth outcome is predicted for the user based on the first correlationvalue. A health service benefit is provided to the user based at leastin part on the predicted health outcome.

Still further, according to another embodiment, a human health knowledgeprofile is determined for each user in a group of users, the humanhealth knowledge profile reflecting that user's independent knowledgeabout assertions in a collection of assertions. Each assertion in thecollection of assertions may pertain to human health and is non-specificas to any user or to any person of the population. At least a firstcorrelation value is determined as between a facet of the knowledgeprofile of individual users in the group of users and a correspondingfacet of the knowledge profile of a control group of persons for whomone or more health outcomes are known. A subset of one or more users isselected based on the first correlation value of each user of the subsetexceeding a threshold designation. A service or designation is providedfor a set value to the one or more users of the subset, and not to otherusers of the group. The service or designation may be associated with atrue per-user cost that is not equal to the set value, but which isvariable and set to increases over time when individual users in thesubset suffer negative health consequences as a result of a naturallyprogressing medical condition. Still further, some embodiments include asystem and method for providing a health service or benefit to a user.By way of examples a health service or benefit can include healthinsurance (including primary or supplemental), life insurance,enrollment in a facility to receive medical attention, medicalpublications, as well as discounts or augmented services thereof. In oneembodiment, a collection of questions are stored, where each question isbased on a documented assertion pertaining to human health. Eachquestion in a first subset may be associated with a correlative healthparameter that is based at least in part on (i) persons in a controlpopulation of that have independent knowledge of an assertion that is abasis of that question, and (ii) a value of a predetermined healthparameter for each person in the control population the value of thepredetermined health parameter for each person being indicative of thatperson's health. Additionally, the second subset of the questions isassociated with a null (i.e. non-existent) or neutral (i.e., notindicative of health) correlative health parameter. A corresponding setof questions is displayed to the user from the collection for responsefor each user in the set of users. A response score is determined foreach user in the set of users based on a correctness of their respectivereply to each question in the corresponding set of questions. A healthparameter value is determined for at least a health outcome based atleast in part on the correlative health parameter of at least somequestions in the corresponding set of questions.

Still further, some embodiments include a system and method forproviding health recommendations to a user. In an embodiment, aplurality of questions are provided to the user. The plurality ofquestions can include multiple questions for each of multiplehealth-related topics, so that individual questions are each associatedwith one or more of the multiple topics. A score is determined for theuser in answering each question in the plurality of questions. The scorecan include topical scores for one or more of the multiple topics. Basedon the topical score of at least a first topic, a set of recommendationscan be identified for the user. The set of recommendations may includean action that the user can perform to improve the user's mental orphysiological health relating to the topic.

According to some embodiments, contextual data is determined from useractivity, and more specifically, from health related activity recordedby a user device. The user device can correspond to, for example, amobile device that the user can carry on their person (e.g., mobiledevice in arm holster), or by a wearable electronic device. By way ofexample, a wearable electronic device can include computerized devicesthat record movement, location, and/or a user's biometric output (e.g.,temperature or heartbeat). Wearable electronic devices can have avariety of form factors, such as, for example, a bracelet, watch, armband, glassware, hat, or garment. Depending on design or implementation,such devices can operate independently or in communication with anothercomputing device (e.g., via Bluetooth or wireless connection to anothermobile computing device).

As used herein, an activity monitoring device includes any electronicdevice which the user can carry, such as a mobile computing device orwearable electronic device, which tracks and records user activity inthe context of health. The recorded activity can include data relatingto user exercise, as well as data relating to everyday activities suchas sleeping, walking, eating, or working (e.g., sitting at desk).According to some embodiments, data generated by one or more activitymonitoring devices is retrieved, and questions displayed to the user arebased on this retrieved data.

While examples such as described are implemented on computer systems,empirical data has been derived to show health outcome prediction can becorrelated to user's knowledge. For example, examples have determinedthat positive health outcome determinations made from evaluating user'sanswers directly correlate to fewer hospital stays.

System Overview

FIG. 1 illustrates a system for predicting a physiological or mentalhealth of a user based on the user's knowledge level of health,according to one or more embodiments. A system 100 as shown by anexample of FIG. 1 can be implemented using a combination of servers, orother computers which combine to provide a network service for clientcomputers operated by a user base. While an example of FIG. 1illustrates the system 100 being implemented as a combination of logicalcomponents, alternative implementations can readily provide forfunctionality described to be integrated or discrete. Moreover, specificcombinations of functionality and processes described can alternativelybe performed as sub-combinations or alternative combinations. Likewise,an example of FIG. 1 illustrates use of multiple data stores, which canlogically and/or physically be implemented as a combined or integrateddata structure (e.g., database), or alternatively, in distributedfashion such as shown.

Among other implementations, system 100 can be accessible to users 11over a network 101, such as the World Wide Web, to mobile computingdevices (e.g., feature phones, tablets, etc.), personal computers (e.g.,desktop computers, laptops, etc.) and other user operated computingdevices for purpose of interactively engaging individual users todetermine their knowledge level on various health topics, and furtherfor predicting the individual user's physiological or mental healthbased on their knowledge level of health. Among other advantages, anexample of FIG. 1 enables facets of physiological or mental health to bedetermined for a person, without need for obtaining user specificmedical information or biological samples. For example, in oneimplementation, a user's health can be predicted without use of anyuser-specific medical question. In a variation, a user's health can bepredicted based only on inputs of gender and age. In another variation,data collected through activity monitoring devices can be used, alone orin combination with other inputs, to predict a user's health.

As described in greater detail, system 100 generates fact-basedquestions on various topics of health for purpose of (i) obtainingresponses from users, (ii) correlating some of those responses tophysiological or mental health determinations, and/or (iii) correlatingsome of those responses to predict a mortality outcome or underwritingclass. One of the underlying assumptions of system 100 is that theliving habits and behaviors of people generally tends to have ameasurable impact on their physiological or mental health, particularlywhen the assumption is applied to a statistically significant sample ofpeople (e.g., hundreds or thousands of persons). Under a statisticallysignificant sample, embodiments described herein have recognized that acorrelation can be made as between the knowledge or awareness ofindividuals and their relative health outcome. More generally,embodiments recognize that health-conscious individuals are generallymore knowledgeable about health and also more healthy as compared toless healthy people (e.g., individuals who suffer from obesity, heartdisease, etc.). In fact, embodiments recognize that healthy individualsare significantly more conscientious of maintaining healthy livinghabits and activities, and with this mindset, such individuals are farmore knowledgeable about health than the rest of the population.

With this recognition, embodiments described herein provide a system forgauging how conscientious a given user is with respect to health, basedon the user's awareness of information that is specific and healthdriven embodiments further recognize that such. Such information, whichin many cases may qualify as trivia, nevertheless provides a mechanismfor delineating those individuals in the population who are in factconscientious about healthy living habits. Furthermore, embodimentsdescribed herein programmatically correlate knowledge of health tophysiologic health of individuals amongst a statistically significantsample size of users. Additionally and/or alternatively, otherembodiments described herein programmatically correlate knowledge ofhealth to a mortality probability for individuals amongst astatistically significant sample size of users. This knowledge can beused for a variety of purposes, such as pricing life insurance (orpremiums).

In order to gauge knowledge, an embodiment of FIG. 1 maintains a libraryof fact-based assertions on various subjects of human health, such asnutrition, exercise, medicine, etc. In an example of FIG. 1 , theassertions are presented to users in the form of questions, for whichresponses can provide answers that are either correct or incorrect, andfurther enable evaluation of knowledge based on whether correct orincorrect answers were given by the users. While examples provide forassertions to be presented to users in the form of questions for purposeof validating their knowledge, other embodiments may use alternativeforms of interaction in order to gauge the user's awareness or knowledgeof a particular assertion. For example, the user may be provided astatement that is presented as an answer, and the interaction requiredof the user can be for the user to generate a question that yields theparticular answer. In this reverse format, the user's ability togenerate the question, combined with a statement as the presentedanswer, serves as a mechanism for determining whether the user hasindependent knowledge of the underlying assertion from which thestatement was originally presented.

Still further, as described in greater detail, some embodiments utilizea collection of assertions, of which only some have been determined tocorrelate to physiological health, mental health, mortality rate orunderwriting class. The user may have no knowledge of which questionscorrelate to health and/or mortality rate, or that only some questionshave direct correlation to health and/or mortality rate while others arebeing provided for alternative purposes (e.g. amusement). In some cases,the user may even have no knowledge that some of the assertions forwhich the user is responding to have any correlation to do with theiractual physiological health, mental health, mortality rate orunderwriting class. Among other benefits, the use of many questions, incombination with questions that have been determined to correlate tophysiological health, mental health, mortality rate or underwritingclass preclude some individuals from ‘gaming’ the questions in a mannerthat masks their true knowledge level and awareness.

In more detail, system 100 includes a user interface 110, questionselection logic 120, response logic 130, health scoring logic 140 a, andmortality scoring logic 140 b. The question selection 120 can receive oraccess questions 127 from a question library 152, and the user interface110 can present content based on the selected questions 127 toindividual users in any one of a variety of computing environments thatstimulate the individual to provide purposeful responses that reflectthe user's understanding and knowledge for a topic of the question. Thequestions 127 can vary in their purpose. In one example, questionlibrary 152 includes (i) a first set of questions 127 a which have beencorrelated to physiological or mental health, (ii) a second set ofquestions 127 b which have not been correlated to physiological ormental health, and (iii) a third set of questions 127 c which have beencorrelated with mortality rate, but which may serve the alternativepurpose of providing trivia, factual information, and/or entertainment.Additionally, the questions of library 152 can be assigned to topics andsub-topics. Still further, the questions of the library 152 can beassociated with a difficulty score, based on, for example, a correctionrate amongst a control group of persons who answered the question.

When the user initiates a session, the user interface 110 may record auser ID 121 and session information 125. In implementation, the userinterface 110 can authenticate the user, and provide credentials 139 fora user profile store 138 in order to obtain profile data 137. Theprofile data 137 can identify, for example, any one or more of (i) thetopic that the user was previously being questioned on, (ii) a topic theuser is interested in, (iii) identifiers to questions that the user aspreviously answered, and/or (iv) a determined knowledge level 135 of theuser. With the profile data 137, the user interface 110 can identifyparameters or other information for facilitating question selections forthe user. In one example, the user interface 110 can use the profileinformation 137 to specify one or more topical parameters 123 and/or theknowledge level 135 of the user. In turn, the question selection 120 canselect questions 127 based on parameters 113, which can be based on, forexample, topic parameter 123, knowledge level 135, or user interestand/or preferences.

The profile data 137 can also include user-specific game data 119 (e.g.,user's personalizations for gaming, historical performance on games,current game play state, etc.). Additionally, the profile data 137 caninclude the user's community or social network data 117 (user'spersonalizations for community or social network application, socialnetwork content, etc.). The user-specific game data 119 and community orsocial network data 117 can, for example, be loaded through therespective functional layers of the user interface 110 when the userinitiates a session with a service of system 100.

In addition to using profile data 137 to create parameters 113, system100 can also use device data 193, which can include indicators of auser's overall health and fitness levels, generated by activitymonitoring devices 191 for parameters 113, alone or in combination withprofile data 137. Activity monitoring devices 191 include electronicdevices (e.g., wearable electronic devices) that can be worn or held byusers 11 in order to track data related to the users' activity levelsand health parameters.

An activity monitoring device 191 can include resources such as GlobalPositioning System (GPS), motion sensors, and/or sensors (e.g.,heartbeat monitor) to record and track user activity, as well asbiometric information of the user in performing such activity.Additionally, the activity monitoring device 191 can include sensorssuch as an accelerometer or accelerometer set, a gyroscope, amagnetometer, an ambient light sensor, heart rate sensor, temperaturesensor and/or other sensors to measure facets of the user's body inperforming an activity. The activity monitoring device 191 recordsactivity data 193, which can include statistics like pace, distance,elevation, route history, heartbeat, body temperature and/or otherinformation relating to the user activity. The activity data 193 caninclude both (i) raw or measured data and (ii) derived or computed databased on measured or raw data and/or user input. Additional examples ofdevice data 193 include heart rate and heart rate trends, steps,distance traveled, floors climbed, calories burned (e.g., derived fromdistance, pace, and user weight/gender), active minutes, sleep quality,blood sugar, and cholesterol levels, among others.

In some aspects, activity monitoring devices 191 can store their data ina device database 192, which can be managed by a computing platform(e.g., APPLE HEALTHKIT, manufactured by APPLE INC.). Such computingplatforms can allow for designated mobile applications to read and writedata to the device database 192 based on a set of permissions. Forexample, the permissions allow a user to choose which applications haveaccess to device data 193 in order to protect privacy and preventunauthorized access to potentially sensitive information. In someimplementations, system 100 may only use device data 193 if a user hasspecifically opted-in and given permission for the data to be accessedby the system.

The user interface 110 can be used to record responses 129 fromindividual users. In one implementation, each question 127 can becommunicated to the user interface using a sequence in which the answerto the question is also packaged and presented to the user. Someconditional logic may also be provided with the question 127, so that,for example, if the user response is correct, the user is instantlynotified and the next question is presented to the user. However, theconditional logic may render an alternative content in response toincorrect user response, specifically a panel or other information itemwhich provides information regarding the actual answer to the questionpresented. In this way, the user is made more knowledgeable.

The responses 129 can correspond to input that identifies, for example,the user's answer to a particular question. The responses 129 canidentify the answer of the user, the question that was answered, and anidentifier of the user. In some implementations, each question 127 canfurther be associated with one or more subject matter topics. Responselogic 130 can process the responses 129 from the various users. In oneimplementation, an initial determination of response logic 130 iswhether the question identified with response 129 is pre-associated witha physiological health, mental health, mortality rate or underwritingclass correlation, or whether no such pre-association physiologicalhealth, mental health, mortality rate or underwriting class correlationexists for the question. In one implementation, the response logic 130records a corresponding response entry 131 for each response, regardlessof whether the question of the response has pre-association withphysiological health, mental health, mortality rate or underwritingclass. The response entry 131 can reflect whether the answer to thequestion is correct, as well as the true answer. In someimplementations, the response entry 131 further links the questionanswered to topical designations for the question, as well ascalibration or difficulty scoring.

Scoring logic 144 can process the answer of response entry 131 todetermine a score value 145 to associate with the particular recordentry. The score value 145 can be based in part on the difficulty levelof the question, which in some implementations, is provided as acalibration coefficient that is pre-associated with the question. Thus,the mathematical process to tabulate scoring can include factors such asthe number of questions the user correctly answered, the number ofquestions the user incorrectly answered, the difficulty parameterassociated with each question, and/or secondary considerations such asthe time it took for the user to provide the response and/or whether theuser correctly answered the question on the first try. The score value145 can be stored with the response data store 118.

Additionally, scoring logic 144 can also tally one or more aggregate oroverall scores for the user based on a historical record of responses.For example, the response data store 118 can maintain one or moreaggregate or ongoing subject matter topical scores (e.g.,weight-lifting), as well as an overall score for the user. As describedwith other examples, scoring logic 144 can be used to developcomparative scoring as between users, based on their overall knowledge,session performance, and/or topical subject matter knowledge.

The response logic 130 can optionally include a knowledge leveldetermination component 134. The knowledge level determination component134 can determine from the response 129 the knowledge level 135 of theuser. Alternatively, the knowledge determination component 134 candetermine the knowledge level of the user from the difficulty parameterassociated with the question and/or with the score output, as providedby the scoring component 144. The knowledge level determinationcomponent 134 can determine an overall knowledge level or atopic-specific knowledge level 135. The determined knowledge level(s)135 can be stored as part of the user profile 138, so that the knowledgelevel of the user is communicated to the questions selection logic 120when the user initiates a session with system 100.

For those selected questions which are identified as having apre-associated physiological health, mental health, mortality rate orunderwriting class correlation, the response logic 130 can provide acorresponding health question record 133 which identifies, for example,the question, the answer provided, and/or whether the question wasanswered correctly. The health question record 133 can also specify atopic or topics of the question.

According to some embodiments, the question identified with the healthquestion record 133 can be associated with a health parameter value 151a and/or mortality parameter value 151 b. As described by otherexamples, the health parameter value 151 a can be determined as part ofa correlative model that is developed using a control population inorder to provide a quantified correlation to physiological or mentalhealth. The mortality parameter value 151 b can be determined as part ofa correlative model that is developed using a control population inorder to provide a quantified correlation to a mortality probability ofan individual. A health scoring database 150 a can maintain a collectionof health parameter values 151 a for individual questions. Additionallyand/or alternatively, a mortality scoring database 150 b can maintain acollection of mortality parameter values 151 b for individual questionsIn one implementation, the health parameter values 151 a reflect apredefined health outcome. In another implementation, the mortalityparameter values 151 b similarly reflects a predefined mortalityoutcome. Multiple health outcomes can be considered, and each questionof health question record 133 can be associated with a particular healthoutcome. By way of examples, the possible health outcomes that havequantifiable correlations to the health parameter values 151 a include(i) health care cost for an individual in a given time period, (ii)number of medical facility visits by an individual in a given timeperiod, (iii) number of prescriptions that the person takes in a giventime period, and/or (iv) number of sick days that the person took. Otherexamples of health outcomes include propensity for cancer (includingcancer of different types), heart disease, diabetes, hypertension orother afflictions. The health outcomes can thus be numerical andcontinuous in nature (e.g., health care cost) or categorical (e.g.,number of medical visits, prescriptions, sick days). In someembodiments, the mortality outcomes may be based in whole or in part onthe health outcomes.

Accordingly, in one implementation, the health scoring component 140 autilizes health outcome logic 142 a to generate a health outcome score165 a that is specific to a particular health outcome definition 155.The health outcome logic 142 a can be implemented as a formula or model,and can take into account parameters that include the health parametervalue 151 a determined from an answered question, the number ofquestions answered, the time of involvement, etc. In one implementation,the health parameter values 151 a that can be combined or tabulatedand/or can be determined from identifying the health questions 141 andresponses 143 of the user. Based on the question and response the healthcorrelative parameters 151 a are retrieved.

Additionally or alternatively, the mortality scoring component 140 butilizes mortality outcome logic 142 b to generate a mortality outcomescore 165 b. The mortality outcome logic 142 b can be implemented as aformula or model, and can take into account parameters that include themortality parameter value 151 b determined from an answered question,the number of questions answered, the time of involvement, etc. In oneimplementation, the mortality parameter values 151 b can be combined ortabulated and/or can be determined from identifying the health questions141 and responses 143 of the user. Based on the question and responsethe mortality correlative parameters 151 b are retrieved. In anembodiment, the health scoring component 140 a uses the healthcorrelation parameter 151 a, as well as the question 141 and response143 to predict the health outcome 165 a of the user. In determining thehealth outcome, the health scoring component 140 a can use a model orformula to determine the health output score 165 a. For example, thehealth scoring component 140 a can map the user's input to a healthscore output which is then predictive for the user. The model used bythe scoring component 140 a to predict the health outcome score 165 a ofthe user can be the same model which determines the correlation ofquestions to the particular health outcome definition. Examples of suchmodels is provided with FIG. 2 .

Additionally, the mortality scoring component 140 b uses the mortalitycorrelation parameter 151 b, as well as the question 141 and response143 to predict the mortality outcome 165 b of the user. In determiningthe mortality outcome, the mortality scoring component 140 b can use amodel or formula to determine the mortality output score 165 b. Forexample, the mortality scoring component 140 b can map the user's inputto a mortality score output which is then predictive for the user. Thehealth outcome score 165 a and/or the mortality outcome score 165 b canbe generated and stored as part of the user health data store 160.Additionally, the health outcome score 165 a can be specific to aparticular health outcome, and the type of value it reflects can bespecific to the health outcome type. For example, one implementationprovides that for a health outcome that reflects health care cost forthe individual, the health outcome score 165 a can be a numericindication of a specific cost or range of costs for the individual. Thehealth outcome score 165 a for the number of medical facility visits, onthe other hand, can be reflected by a category or level (e.g., 1 to 5depending on amount).

In one implementation, the user health data store 160 is maintainedlogically or physically separate from the question response data store118 in order to preclude its viewability to users of the system 100.Each user can include a profile of health outcome scores with the userhealth data store 160, with individual user profiles 141 which includescores for multiple different health outcomes. In some variations, acombined score or category may also be given to individual users as partof their health profile.

As described with other embodiments, the health outcome score(s) 165 aand/or mortality outcome scores 165 b of the user can be made availablefor health services, such as health insurance services. For example, thepremium, deductible or scope of coverage provided as part of a healthinsurance package for a user can be determined from the health outcomescore(s) 165 a and/or mortality outcome score 165 b. As another feature,health outcome score(s) 165 a and/or mortality outcome score(s) 165 b ofthe user can be used to determine if the user should receive a discountfor health insurance, or alternatively receive an added benefit fromhealth related services that are provided (or are to be provided) to theuser.

According to one embodiment, a health service 190 sub-system can utilizethe health outcome scores 165 a and/or mortality outcome scores 165 bprovided in the user health database 160 to determine designations,qualifications or service level, in connection with a health-relatedservice. Examples of health related services 190 include healthinsurance, life insurance, health service plans, memberships in healthrelated facilities (e.g., health spas, medical office), informationalservices (e.g., magazine or journal subscriptions, electronic news). Thebenefit that can be provided to the user includes the service itself, oralternatively a designation of health for use with such a service. Forexample, the user's predicted level of health can be determined by thehealth outcome score(s) 165 a, and this can result in an overall healthoutcome determination (e.g., a ranking or classification), which in turncan be used to receive a discount for health related services (e.g.,discount on health or life insurance premium, expanded coverage, etc.).An example of health service sub-system is provided with an example ofFIG. 6B.

In some implementations, the user interface 110 of system 100 caninclude various layers or functional components for enhancing theinteractivity level of the user. In one implementation, the userinterface 110 includes a gamification layer 112 and a community socialnetwork layer 114. The gamification layer 112 includes logic, data, andcontent (collectively “game data 103”) for implementing a competitiveenvironment for which the individual is to supply answers for questions127. The game data 103 can be generated a gaming engine 115, which canfurther personalize the gaming environment for the specific user. Forexample, the user identifier 121 can be used by a gaming engine 115 togenerate user-specific game data 103. The game data 103 can, forexample, include a competitive environment that is based on theknowledge level of the user and/or topical interests of the user. Animplementation that utilizes a gamification layer 112 is described withFIG. 7A. The gamification layer 112 can determine awards or credentials(e.g., skill level badges) for the user based on their performance. Byway of example, the questions 127 presented through the user interface110 can be associated with a score value that accounts for difficulty(which may be determined from a calibration process, as detailed below),response time, handicaps (e.g., the age of the user), etc.

The community social network layer 114 can operate using community data117, which can be generated from a community/social network service 116.The community/social network service 116 can, for example, provideuser-specific community (or social network) data based on the useridentifier 121. The community data 117 can provide content (e.g., user'shealth interest or knowledge specialties) that is provided as part ofthe community social network layer 114.

The health parameter value 151 a and mortality parameter value 151 brepresent a correlative and quantified measure as between human healthand knowledge of a particular assertion. The granularity of the healthparameter value 151 a and/or mortality parameter value 151 b is appliedto a question as answered from an individual, but the determination ofthe value can be based on a correlative model applied to a controlpopulation of users. The control population of users include thoseindividuals who, for example, voluntarily provide real-world informationabout themselves, and more specifically, actual health outcomes in arecent duration of time. The control population of user may also includethose individuals who are deemed to be deceased at a particular point intime. Activities of those individuals can be evaluated retroactively todetermine whether a mortality correlation exists (as well as the degreeof correlation) with respect to activities. By way of example, theactivities can include participation in health quizzes and tutorials.The source for data in the mortality control group can include onlinesocial media sites, and obituary services.

In more detail, system 100 can include a question analysis sub-system170 that includes functionality for determining correlations betweenknowledge of individual questions and actual health outcomes and/ormortality outcomes. The sub-system 170 can implement and develop one ormore correlative models 172, which can analyze input questions 171 forthe purpose of determining correlations to health outcomes and/ormortality outcomes. In particular, the correlative models can beimplemented for purpose of determining health parameter values 149 aand/or mortality parameter values 149 b that statistically reflect acorrelation as between knowledge of individuals in the controlpopulation (shown with the control population database 180) forparticular question and the respective health and/or mortality outcomesfor those individuals who answered the question (either correctly orincorrectly, depending on implementation). The health correlative values151 a and/or the mortality correlative values 151 b can be specific toindividual questions or cluster of questions. In one implementation,different correlative models 172 can be used for different types ofhealth outcomes. Different correlative models may compare a predictedvalue with actual (or real-world) data provided for individuals (shownas real-world information 175). The real-world information 175 may alsoinclude actual (e.g., real-world) data from social media sites and/orobituaries. An example of question analysis sub-system is described inmore detail with an example of FIG. 2 .

In addition, the control population database 180 may include apopulation of individuals that make up a control group that can be usedto compare a true health outcome and/or mortality outcome to expectedoutcomes. The expected outcomes can be a function of the healthparameter values 149 a and/or the mortality parameter values 149 b thatdetermines correlations between knowledge of individual questions andactual health outcomes and/or mortality outcomes. In an embodiment, thecontrol group may be dynamic, such that individuals can be added to thecontrol population database 180 continuously over time through voluntaryopt-in features or invitation. Additionally and/or alternatively, thecontrol population database 180 may store individuals who have beenadded, including individuals who have been added in the past (e.g.,months, years, or decades ago). Accordingly, some individuals who areincluded in the control population database 180 for a long period oftime may have, since being added, become deceased. These deceasedindividuals can provide real-world information 175 about actualmortality outcomes. An example of providing real-world mortalityinformation to the system 100 is described in more detail with anexample of FIG. 10 . As more individuals are added to the controlpopulation database 180, the correlation(s) for health and/or mortalitymay be made more valid or certain. While numerous examples provide foruse of health and/or mortality correlative scores, other embodiments canalso generate recommendations to users based on their overall knowledgelevel, as determined by, for example, the user's score, ortopic-specific scores. A response analysis 164 can retrieve scores 145from the response database 118, for example, and generaterecommendations, content or other output based on the user scores. Anexample of response analysis 164 is illustrated with FIG. 7B, andaccompanying examples thereof.

As an addition or alternative, the community social network layer 114can provide forums such as message boards, ask an expert, or topicalwalls for shared information and experiences. In one implementation,credentials that the user earns through the gamification layer 112 arecarried onto the social environment of the community social networklayer 114. For example, an ‘expert level’ user may have credence whenresponding to questions of others, even to a point where the user canrequest payment or other consideration for providing answers orinformation to other users.

FIG. 2 illustrates an analysis system, according to an embodiment. Inparticular, FIG. 2 illustrates an analysis system 200 for analyzingquestions (or other forms of assertions) for purpose of determiningwhether knowledge of the underlying assertions by subjects can becorrelated to physiological or mental health of the subjects. Accordingto some embodiments, individual questions, or alternatively groups ofquestions, can be correlated to a quantifiable metric that statisticallyrelates a subject's knowledge (or lack of knowledge) for an underlyingassertion to a likelihood of a particular health outcome. The system 200can be implemented as, for example, a sub-system of aphysiological/mental predictive system 100, such as shown with anexample of FIG. 1 .

In more detail, system 200 includes a question intake interface 210, afielder 220, a calibration component 230, and a correlative modelimplementation component 250. A question interface can receive questions209 through, for example, a manual interface (e.g., experts generatequestions based on health assertions). The questions 209 can be manuallyassociated with one or more topics relating to human health, such astopics relating to nutrition or exercise, or specific medicalconditions. The granularity of the topics 211 can be determined byimplementation. A question store 215 can be used to store a question 209for processing as the question is calibrated and/or correlated to humanhealth.

The fielder 220 includes functionality to distribute the questions 209to a control population of users through a population interface 222. Forexample, the fielder 220 can issue questions using the user interface110 of an example system of FIG. 1 . For example, with further referenceto an example of FIG. 1 , questions 209 can be issued through gameplayof user interface 110, and responses from various users can be recorded.Some users, however, can be designated as belonging to the controlgroup. These users can correspond to individuals for which datacorresponding to ground truth data exists. For example, many users canbe given an opportunity to volunteer real-world health information. Suchusers can be asked questions such as “how many doctor visits did youhave last year” or “how many sick days did you have last year.” Stillfurther, some information like the user's health insurance cost can beobtained from a source such as the insurance companies. Accordingly, inone example such as shown by FIG. 1 , members of the control group cansupply responses 213 to questions 209, presented through a game. At aseparate time, either before or after the questions 209 is presented tothe subject, the subject can also be given the choice to provide actualdata, shown as true user data 241. The true user data 241 can representan actual health outcome of a subject providing the response 213. Thetrue user data 241 can include information manually supplied by thesubject, as well as information provided by, for example, an insurancecarrier of the subject. Each response 213 from one of the subjects ofthe control population (e.g., those users of system 100 who opt-in toprovide information) can be linked to the question and to the identifier205 of the subject. Additionally, the true user data 241 can be linkedto the user identifier 205 of the subject providing the response.

In addition, true user data 241 can be supplemented or replaced withinformation gathered by activity monitoring devices 225 in order tocreate more accurate control data. Activity monitoring devices 225 canprovide health data 226 from sensors, such as heart rate and heart ratetrends, calories burned, active minutes, sleep quality, blood sugar, orcholesterol levels. Location data 227 can also be provided and includeswhere a user is located based on GPS data, which can be used inconjunction with other databases to determine, for example, if a user isin a restaurant, grocery store, etc. Furthermore, time data 228 can beused to track a user's schedule. In addition, a user can also choosequestions to send to a friend through their activity monitoring devices225 as friend data 229.

In some embodiments, some or all of these data gathered from activitymonitoring devices 225 can be used by fielder 220 to choose whichquestions 209 are presented to a user. For example, if health data 226shows that a user has high blood pressure, questions relating to how tolower blood pressure can be chosen. If the user is shown to have poorsleep quality, questions about tips to get better sleep can be chosen.If the user has just finished a workout, questions about post-workoutrecovery can be chosen. If a user is determined to be a new runner,questions about basic running knowledge can be chosen, whereas if a useris an advanced runner, more advanced questions can be chosen instead.

Location data 227 and time data 228 can also be used by fielder 220 tointerpret a user's schedule and choose appropriate schedule-relatedquestions. For example, if the data show that a user commutes via a longsubway ride every weekday, questions about exercise ideas for longcommuters can be shown. If a user is detected in a restaurant, questionsregarding healthy food choices can be shown, and if a user is in agrocery store, questions about vegetables, organic food, and nutritioncan be shown.

The calibration component 230 can analyze the questions 209 underprocess to determine a difficulty level 265 of the question. Forexample, the calibration component 230 can query 231 the intake store215 for a tally of the number of responses which were correct andincorrect. The percentage of individuals who correctly answer a questioncan provide a basis for determining a difficulty level of the question.The difficulty level 265 can be stored with the question for subsequentuse.

The correlation model 250 operates to determine a correlation betweenknowledge by a subject for an underlying assertion of a question and thesubject's health. In one implementation, the correlation model 250implements one or multiple models for purpose of determining differentparametric values that statistically correlate to different healthoutcome definitions (e.g., amount of healthcare or healthcare cost anindividual requires, the number of medical facility visits, propensityfor heart disease, cancer, hypertension or diabetes, etc.). Thecorrelation model 250 can receive, as model input 255, (i) a questionidentifier 261, (ii) identification of a set of individuals in thecontrol group who answered the question 209, including identification ofthe answer each person provided to the question 209, and (iii) true userdata 241 for each person in the set of individuals that answered thequestion. The particular model selected compares an expected result to atrue result by (i) assigning the person to an expected result,corresponding to a particular health parameter value, based on theiranswer to a question, then (ii) using the true user data 241 to comparea true health outcome (reflecting real-world data of the individualsupplying the answer) to the expected result.

The expected result can initially start as a hypothetical or neutralvalue, indicating a likelihood that a given person has or does not havea particular health outcome based on the answer the person provided tothe question. The expected result can further include different valuesdepending on whether the user provided a correct answer or incorrectanswer, as well as which incorrect answer the user provided. The initialcorrelation can correspond to a coefficient (e.g., a value between 0and 1) that is set by, for example, an expectation as to whether theunderlying assertion of the question is information that is indicativeof health-conscious behavior (e.g., rubbing one's eyes can make a personsusceptible to common cold) or information that is indicative of poorhealth-conscious behavior (e.g., specific nutritional information abouta donut). From the initial value, the correlation can become positive,negative or made neutral based on the expected/actual comparison forpersons in the set. As more individuals are added to the set, thecorrelation can be made more valid or certain. The determinedcorrelation from the correlation model 250 can be identified ascorrelative health parameter 251. The correlative health parameter 251can be specific to a particular health outcome 253. The correlativehealth parameter 251 can, for example, correspond to a parametric value,such as a weight or coefficient, which can be aggregated, modeled and/orcombined with other parametric values to make a health outcomedetermination.

The particular model 255 implemented by correlation model 250 can dependon the nature of the health outcome that the assertion is to apply to.For a health outcome definition in which the health parameter value iscontinuous (e.g., monetary cost for health care in a given period,weight or body mass index), a linear regression model can be used. Somehealth outcome definitions can utilize health parameter values which aretiered or categorical. For example, the number of medical facilityvisits can be defined into tiered values, such as: 0=no medical facilityvisits, 1=1-2 medical facility visits in a year, 2=3-5 medical facilityvisits in a year, or 4=5 or more medical facility visits in a year.Similar tiered values can be used for health outcomes such as sick days.For such health outcomes, an ordinal logistic regression model can beused. In variations, a multinomial or polynomial model can be used fortiered categories, particularly those health outcomes which define tierswhich are not naturally ordered. Each question can be assigned to aparticular health outcome, so that the health parameter value isspecific to the determination of the health outcome.

Numerous other machine-learning models can be used in both developingcorrelative health parameters, and determining health outcomes based oncorrelative health parameters. By way of example, such machine-learningmodels can include random forest, neural network and/or gradientboosting models.

In some embodiments, the determination of the health parameter values251 can be tuned to reflect determinations that are for use with a modelin which no user-specific information is known. In one implementation,the control population can be associated with classification parameters,such as age group (e.g., over 50, under 50), gender, weight, race,geographic location or setting, and/or presence of certain medicalconditions such as diabetes. An individual question can be associatedwith multiple correlative health parameter values 251, including healthparameter values that reflect the general control population, as well asa health parameter value that is specific to a class or sub-class (e.g.,females over 50).

According to some embodiments, a combination of question and correlativehealth values 251 can map to one of multiple possible health outcomes.Thus, in one implementation, a question can have a correlative healthvalue as it applies to a single health outcome.

Other implementations provide tor the determination of health parametervalues 251 which are correlative to health of a user based on a model inwhich a classification (e.g., gender or age) or set of classifications(e.g., gender and age) are known about the person answering thequestion. Depending on implementation, the classifications of users caninclude (i) unknown users, for which no information is known, (ii) usersfor which some basic health-relevant characteristic is known, such asage, gender, or combination thereof, (iii) users for which multiplerelevant facets of health is known, such as their weight and/or height,as well as, as gender and age. One implementation provides for thedetermination of correlative health parameters 251 which are determinedspecific for different classifications of the user, based on applyingmodels as described to segments of the control population which have therelevant classification. Thus, in some variations, the correlativehealth parameter values 251 can be made specific to specific classes ofpersons, so that the evaluation of health for the user is made inreference to the user's class. For example, in some embodiments, thequestions can be fielded for individuals who categorize themselves bygender, age, weight, and/or presence of certain medical conditions suchas diabetes.

System 200 can be implemented on a control group that is dynamic,meaning individuals can be added to the control group continuously overtime. As mentioned, a larger control group can provide more validresults. In an interactive gaming environment, such as described with anexample of FIG. 1 , additional persons can be added to the control groupcontinuously through invitation or opt-in features. For example, theuser-interface 110 can prompt individuals to volunteer for questionsthat reflect actual medically relevant information. This mechanism canprovide a way to expand the control group with the addition of users forwhom true user data 241 can be provided. The control group can also bemanaged based on criteria, such as gender and age, so that it accuratelyreflects a desired population segment.

With the determination of the health parameter values 251, the questionscan be deemed processed, in which case the questions can be included ina library or collection of questions and marked as being correlative tohealth. In one implementation, a library build process 260 linksprocessed questions 259 with the question identifier 261, topicalidentifier, the difficulty level 265 and the correlative healthparameter 251 (or multiple values). The difficulty level 265 can be usedto determine which individuals receive the question based on user level.

While an example of FIG. 2 provides for processing of questions whichare deemed correlative to health, a fielding and calibration process canbe used to determine difficulty of all questions, including thosequestions which have no determined correlation to health. For example,any question can be associated with the topic 211 and fielded to thecontrol population as described, and further evaluated for difficultylevel 265 based on, for example, the percentage of individuals of thecontrol group who correctly answered the question.

FIG. 3 illustrates an example of a data structure that can be developedto link a question with a health outcome and a topic, according to oneor more embodiments. While an example of FIG. 3 illustrates the datastructure 300 as being logically integrated, variations can provide fordistributed data structures which associate or link parameters asdescribed. With reference to an example of FIG. 1 , the data structure300 of an example of FIG. 3 can, for example, be provided with thequestion library 152, and include information provided with the healthscoring database 150 a.

In more detail, data structure 300 associates individual questions byquestion identifier 301 to one of multiple possible correlative healthparameters 303, and one or more topics 305. Other information orparameters that can be conveyed with the data structure 300 include adifficulty level, which can be determined, for example, through anoutput of the calibration component 230 (see FIG. 2 ). For a givenimplementation, the correlative health parameter can relate to aparticular health outcome. Multiple health outcomes can be defined for afuture time interval, including health care cost, medical facilityvisits, sick days, and number of prescriptions. Other examples of healthoutcomes include blood sugar level, weight or body fat (e.g., BMI),cholesterol level, depression or anxiety disorder, and/or longevity. Inone embodiment, each question associated to only one health outcome, andis further assigned a correlative health parameter value that reflects acorrelative measure between knowledge of the underlying assertion and acorresponding health outcome. In one implementation, a system of FIG. 2determines health parameter values for each defined health outcome, andthe health parameter value selected for a question is that which has thestrongest correlation. If no correlative health determination has beenmade for a question, then the health parameter values for such questionscan be shown as null.

As further shown by an example of FIG. 3 , each question can be linkedwith multiple topics based on, for example, manual input. The determineddifficulty can also be expressed as a parameter, such as a numberbetween 0 and 1. The difficulty level can be independent of the topicassignment for the question-thus, meaning the difficulty level of aquestion can be provided as being the same regardless of the assignedtopic being considered.

Methodology

FIG. 4 illustrates an example method for predicting a health outcome ofa user based in part on whether a user has independent knowledge of anassertion relating to health. FIG. 5 illustrates an example method forpredicting a health outcome of a user based on a knowledge profile of auser. In describing example methods of FIG. 4 and FIG. 5 , reference maybe made to elements of FIG. 1 , FIG. 2 or FIG. 3 for purpose ofillustrating a suitable component for performing a step or sub-stepbeing described.

With reference to an example of FIG. 4 , a collection of assertionsrelating to human health can be stored and processed for use with apopulation of users (410). In one implementation, the assertions can beformatted as questions for which the answer from the user indicateswhether the user has knowledge of the assertion (412).

For the control population, a health parameter value is determined forindividuals of the control population (420). The health parameter valuecan reference actual or real-world data which serves as an indicator ofphysiological or mental health of a user. In one implementation, thedetermination of the health parameter value can be based on input of auser. For example, in an interactive gaming environment of FIG. 1 , someusers can opt-in to provide requested health-specific input, such as thenumber of sick days taken in the prior month or year. In someembodiments, the health parameter value is based on a defined healthoutcome (422), or combinations of health outcomes. By way of example,the health outcome can correspond to an estimated health care cost foran individual (424), a number of medical center visits for an individualin a given duration of time (425), a number of prescriptions for theindividual in the given time frame (426), and a number of sick days anindividual incurred in the given duration of time (428).

For each assertion, a correlative health parameter is determined (430).Generally, the correlative health parameter corresponds to a parametricmeasure that quantifiably links knowledge of an assertion to humanhealth. The health parameter value 151 a (FIG. 1 ), 251 (FIG. 2 ), asdescribed with other examples, provides an example based on use of acontrol group (432).

The establishment of questions with associated correlative healthparameters can be done through implementation of a model, with groundtruth data provided by select users from a larger user base ofrespondents. Once the correlative health parameters are established forindividual questions, the questions can be fielded to the user base. Theresponses from the user can be used to determine the user's independentknowledge level of a particular assertion (440).

The correlative health parameters for the individual questions answeredby the user can be determined and modeled into a value for a particularhealth outcome (450). For each user, the correlative health parametersof the answered questions pertaining to a particular health outcome canserve as inputs in order to determine a predicted health outcome for theuser (460). Multiple health outcomes can be determined in this manner.

With reference to FIG. 5 , a knowledge profile of a user can bedetermined, relating to a particular health outcome (510). The knowledgeprofile can reflect answers to individual questions, or answers toclusters or groups of questions. The knowledge profile can be determinedbased on a selected definition. In one implementation, the knowledgeprofile is specific to a question, and reflects whether a user correctlyanswer the question. In a variation, the knowledge profile is specificto a question, and reflects which question the user answered. Stillfurther, the knowledge profile can reflect the user's answers inaggregate form, such as in a cluster of questions (e.g., 3 to 10questions), reflecting facets such as the number of questions the usercorrectly answered in the cluster, or the number of answers providedwhich were deemed more wrong than others.

A facet of the knowledge profile can be compared to corresponding facetsof knowledge profiles from individuals of a control group (520). In oneimplementation, the user's answer to a particular set of questions canbe individually compared to an answer to the same set of questions fromone or multiple persons of the control group. In variations, the user'sanswer to a cluster of questions can be compared to answers provided bya subset of the control group for the same cluster of questions, withthe comparison being made for the cluster of questions as a whole. Stillfurther, the user's answers can be compared to answers provided by asubset of the control group which provided the same exact answers forthe cluster of questions.

A health outcome can be determined for individuals of the control group(530). As mentioned with other examples, the health outcome can bedefined as a healthy living style characteristic that is indicative ofhuman health. The health outcome that is determined for a person of thecontrol group can reflect real-world information about that person(532). In one implementation, individuals of the control group canvolunteer their personal health outcome information (534). For example,the information can be provided in exchange for some benefit to theperson of the control group. In other examples, personal health outcomeinformation for persons in the control group can be determined usingdata from activity monitoring devices. In variations, the healthoutcomes information for persons of the control group can be determinedfrom sources such as health care or insurance providers (536).

The health outcome of a user can be predicted based in part on acorrelation between the health outcomes of individuals in the controlpopulation and the compared facets of the knowledge profile between theuser and persons of the control group (540). Thus for example, a user'sanswer to individual questions can be compared to the answers providedfor the same questions by those members of the control group. As anaddition or alternative, a user's answers to a cluster of questions canbe compared to answers provided to the same cluster of questions forindividuals of the control group, with, for example, the comparisonbeing based on matching the user with a subset of persons of the controlgroup based on a percentage of correct or incorrect answers provided.

Health Service Methodology and Sub-System

FIG. 6A illustrates an example method for providing a health relatedservice to a user based on a knowledge-predicted health outcome for auser. In describing example method of FIG. 6A, reference may be made toelements of FIG. 1 , FIG. 2 or FIG. 3 for purpose of illustrating asuitable component for performing a step or sub-step being described.

With reference to FIG. 6A, a health knowledge profile is determined fromeach of multiple users (610) with regard to assertions relating tohealth (e.g., physiological or mental health). As mentioned with otherexamples, the health knowledge profile can reflect individual answers toquestions, those questions which were answered correctly or incorrectly,specific answers provided to specific questions (e.g., such as incorrectanswers), and/or percentages of questions answered from a definedcluster of questions.

Additionally, as mentioned with other examples, a value of a healthcorrelation parameter can be determined as between the user and a subsetof persons in the control group (620). With reference to an example ofFIG. 1 , the health value parameter 151 a can, for example, bedetermined by the health scoring component 140 a. In determining thehealth correlation parameter, a given facet of the users' knowledgeprofile can be compared to that of relevant persons in the control group(622). By way of example, the comparison can be on a question byquestion basis, or alternatively, on a cluster basis (e.g., compare setof 5 answers, etc.). Actual health outcomes can be known for members ofthe control group, and the identified correlative health parameters canbe based in part on the known health parameters of individuals in thecontrol group. The correlative health parameter can thus bepre-determined for the control group, and based on real-worldinformation about members of the control group.

Based on the correlation values, a health outcome determination isprovided for the user (630). As shown with an example of FIG. 3 , thecorrelation values can be specific to pre-determined health outcomes.Further with reference to an example of FIG. 1 , given a set of healthparameter values 151 a for a particular health outcome, the healthscoring component 140 a can make a health outcome determination. Thedetermination of the health outcome can be in the form of a score, sothat it gives a relative measure of the particular health outcome ascompared to other individuals in the general population. The healthoutcome determination can correspond to a health outcome score 165 a, oralternatively, to a combination of health outcome scores. For example,multiple health outcome scores can be determined for the user, and thescores can be combined to form an aggregate health outcomedetermination.

Based on the health outcome determination, a health service benefit canbe provided to the user (640). The service or designation can be onemade for a set value, wherein the service or designation is associatedwith a true per-user cost that is not equal to the set value, but whichis variable and set to increases over time when individual users in thesubset suffer negative health consequences as a result of a naturallyprogressing medical condition.

The health service benefit can correspond to a variety of direct andindirect service related benefits. In one implementation, those userswith a health outcome determination that exceeds a particular thresholdcan receive a designation (642). The designation can correspond to aservice or credential provided to only select users of, for example, anetwork service provided with system 100 (644). For example, those userswhich receive a health outcome determination that places them within thetop 10 percentile of all users may receive a certification, which inturn enables them to receiving discounts with their healthcare provider,health insurance, or related health service activities (e.g., discountwith nutrition store, athletic gym membership, life insurance, etc.).Alternatively, the designation can entitle the subset of users toreceive a service, such as primary health insurance, supplementalaccidental insurance, life insurance, or other membership service(whether health related or not).

In variations, the health outcome determination provides a basis forpredicting a user's health, and this basis can in turn be used todetermine health related services for the user (646). For example,health insurance, life insurance, and/or accidental health insurance canbe provided to the user with scope and cost determined by the healthoutcome determination. For example, the cost of the premium ordeductible to the individual user can be based on the health outcomedetermination (648). By way of example, an insurance service can beprovided to users of system 100, and those users with better healthoutcome determinations can be provided discounts to their premiums ordeductibles, or alternatively given greater scope of coverage ascompared to counterpart users who have lesser health outcomedeterminations.

FIG. 6B illustrates a health service sub-system 680, according to anembodiment. A health service sub-system 680 can be implemented with oras part of, for example, system 100. In variations, the health servicesub-system 680 can be provided as a separate system which interfaceswith the system 100. Additionally, the health service sub-system 680provides an example of a system on which an example of FIG. 6 can beimplemented.

With reference to FIG. 6B, a health service sub-system 680 includes asystem interface 682, a customer data store 684, and servicedetermination logic 686. The health service sub-system 680 can alsoinclude a service customer interface 688, such as a web page orapplication page, which a service customer accesses to provide input fordefining the health service offered, as well specific logic orparameters for the service determination logic 686. The service customerinput 685 can, for example, include text data definition of the serviceoffered (e.g., terms of health or life insurance), as well asupplemental content for viewing by users of system 100. This input canbe stored in the service data store 684.

In some variations, the service customer input 685 can further inputparameters 683 and other logic (e.g., rules) for the servicedetermination logic 686. The parameters 683 and rules can, for example,including definition of the qualifications needed for users to (i)receive the service, (ii) receive a particular facet or tier of theservice, and/or (iii) receive the service or tier according to aparticular price structure. For example, the service can include tiersof benefits, or multi-tiered cost structure, and each tier can beprovided to users based on qualifications, such as one or more of (i) athreshold health outcome score or set of scores, (ii) a thresholdcombination of health outcome score, and/or (iii) other health outcomedetermination.

The system interface 682 can interface with the user health database 680in order to determine the health outcome scores 689 of a given user oruser-base. In a variation, the system interface 682 can communicate witha push or trigger component on the system 100 which in turn retrievesand pushes specified health outcome scores to the system interface 682.In some embodiments, end-users are precluded from handling healthoutcome data. The output of health determination logic 686 cancorrespond to a notification 691, which can specify the results of thehealth determination logic 686. These results can be communicated toeither the user or to a provider of the health service benefit.

Game Play

Numerous embodiments described use of game play and logic as a mechanismto increase use response and participation. More user response andparticipation can have numerous benefits, including (i) increasing thesize of the control group, by finding more qualified volunteers who arewilling to provide real-world health information for purpose ofdeveloping health correlations to questions, (ii) more predictivecorrelations based on larger statistical sample, and (iii) data pointsfrom users, enabling better prediction of individual user health.Additionally, the use of game logic provides a mechanism to hide healthcorrelative questions from public inspection, thereby precluding usersfrom “gaming” the questions (e.g., studying) for purpose of receiving agood health score.

FIG. 7A illustrates an example method for providing a game-basedenvironment in which user responses enable prediction of health outcomesfor individual users. In describing an example method of FIG. 7A,reference may be made to elements of FIG. 1 , FIG. 2 or FIG. 3 forpurpose of illustrating a suitable component for performing a step orsub-step being described.

With reference to an example of FIG. 7A, a set of questions can bestored, were at least some of the questions are based on assertions thatare core relative to health (702). For example, questions can be storedin the question library 152, after being processed using a system suchas described with an example of FIG. 2 . The stored questions caninclude both (i) health correlative questions, which are used indetermining a health outcome score or determination for the user (704);and (ii) non-health correlative questions. While the latter questionsmay pertain to health, those questions have either not been determinedto be correlative or health, or those questions have little relevance toawareness for health, and thus correlative to actual human health (706).As mentioned with other examples, a gaming environment can beimplemented in which the questions are provided as trivia, so that usersreceive entertainment benefit from participating in answering questions.

Still further, as described with other examples, the health correlativequestions can be processed to determine a health correlative parameter(710). For example, question analysis sub-system 200 can be used todetermine a health correlative parameter 151 a for a given question.Still further, as described with other examples, the health correlativeparameter can be based on persons in the control population who haveknowledge (or knowledge deficit thereof) of an assertion underlying theparticular question (712).

In order to encourage participation and development of accurate healthoutcome scores and determinations, a gaming environment can beestablished in which users are asked questions in a competitive orsemi-competitive context (720). An example of a gaming environment isshown with environments depicted through interfaces of FIG. 8A through8H.

The user responses to trivia questions are recorded, with thoseresponses including both scores related to health correlative questions(730) and scores related to all questions (or alternatively tonon-health correlative scores) (732). As described with an example ofFIG. 6 , the health correlative questions can be scored for purpose ofdetermining health services to the user (740). This score may be hiddenor unknown to the user, and determine independently of the overallgaming score.

Conversely, the overall gaming score can be published in a social orgaming environment, to provide the user with credentials in thecommunity of the service provided through system 100 (742). For example,the user can use the latter gaming score to achieve credentials thatgive the user authority on message board discussions, and question andanswer forums of the community platform.

In some variations, the gaming score can also provide a mechanism toprovide health base recommendations to the user (744). For example, theuser's knowledge base can be evaluated based on topical subjects, andthe user's deficiency or strengths respect to specific topics of healthcan be used to infer physiological or mental information about the user.

FIG. 7B illustrates a knowledge-based recommendation engine, accordingto one or more embodiments. With reference to FIG. 1 and FIG. 7B, forexample, the response analysis component 164 can include recommendationengine 780. The recommendation engine 780 can use information about theuser's knowledge in order to generate recommendations 785, which caninclude content that communicates to the user specific actions,lifestyle choices, or areas of growth (for knowledge or lifestyle), forpurpose of growth.

In one implementation, the recommendations 785 can be based on thedeterminations of the user's strength or weakness with regards tospecific topics of health. The recommendation engine 780 can includeprocesses 782 which retrieve the user's topical scores 781, and thencorrelate the topical scores with recommendation logic 790. Therecommendation logic 790 can include rules 791, 793 for selectingrecommendations for the user based on different topical scores andcriteria. For example, the recommendation logic 790 can include rulesfor suggesting recommendations to users for specific topics when theuser's score for the topic is below a threshold. By way of example, atopic can be defined for cardiac health, and anytime a user's topicalscore for cardiac health is below a threshold, a set of recommendations785 for improving the user's cardiac health can be generated andcommunicated to the user. Likewise, if the user's knowledge is strong ina particular topic, that can also be interpreted as interest, and therecommendation logic 790 can utilize the score to suggestrecommendations that are of an advanced level. For example, if the userscores high in the topic of weight lifting, then the recommendationprovided to the user can include specific techniques or recommendationsbased on questions that have the highest difficulty level (as determinedfrom, for example, a calibration component 230 of FIG. 2 ).

In some implementations, activity monitoring devices 770 can providedevice data 774, which can include indicators of a user's overall healthand fitness levels, to the recommendation logic 790. These devices caninclude GPS receivers to record statistics like pace, distance,elevation, route history and workout summaries. In addition, they caninclude sensors such as accelerometers, a gyroscope, a compass, anambient light sensor, heart rate sensor, among other features capable oftracking and recording health and fitness parameters. Examples of devicedata 774 include heart rate and heart rate trends, steps, distancetraveled, floors climbed, calories burned, active minutes, sleepquality, blood sugar, and cholesterol levels, among others.Recommendation logic 790 can then use device 774, alone or incombination with rules 791,793 and topical scores 781, in order tocreate the recommendation set 785. For example, if topical scores 781show that a user has poor knowledge of cholesterol but device data 774indicates that the user's cholesterol levels are satisfactory,recommendation logic 790 may choose not to recommend cholesterol-relatedquestions.

In a variation, the set of recommendations 785 generated for any onetopic can be associated linked with questions or sub-topics ofquestions. A recommendation filter 792 can filter the recommendations785, so as to weed out those recommendations the user likely knows basedon their correctly answered questions.

Still further, the recommendation logic 790 can include combinationrules, which select recommendations 785 for the user based on criterionprovided by the user's topical score in two or more topics. Thecombination rules can identify subject matter relevancy between topics,so that the user's knowledge of one topic will benefit another or viceversa. In one implementation, when the user's topical score of one topicexceeds a threshold, and the topical score of another topic is below athreshold, then the recommendation may be provided that assumes useractivity or interest in one topic to assist the user's knowledge orlifestyle with regards to the second topic. For example, the user mayhave scored high in the topic of weight-lifting, but scored low innutrition or sleep. The recommendation provided to the user may identifythe recommended hours for the user to sleep in order to add muscle mass.

By way of another example, if the user is strong on a subject such asweight training, but poor in nutrition, then the recommendation enginecan suggest (i) that the user develop his knowledge on nutrition, (ii)identify nutritional information related to training in order to providerecommendations. Recommendations can include, for example, what the usershould eat when training, how such nutritional intake can affectperformance in training, recommendations for the user to confirm with anutritionist, and expected results that can be achieved through properdiet and weight training. Such an example illustrates recommendationsthat can be made based on the user being strong in his or her knowledgebase for one topic and weak in another topic. In such scenarios, therelationship between the two topics can be determined in order togenerate programmatically actions and subtopics of learning which may beof interest or benefit to the user.

Similar recommendations can be determined and linked to user's topicalscores based on different threshold determinations. In oneimplementation, if the user scores low on two topics related by subjectmatter, the user's recommendation may be selected on the assumption thatthe user suffers from health consequences related to a physiological ormental problem related to the topics.

Still further, analysis of the topical determinations can also be usedto infer characteristics about the respondents, without any mathematicalcorrelation being made to the control population. For example, anindividual who scores poorly in both nutrition and exercise can beinferred to be obese, potentially diabetic, and/or suffer from otherhealth related issues such as depression. Based on such analysis, therecommendation engine can suggest areas of growth for the user'sknowledge. The recommendation engine 780 can also provide recommendedactions, such as publishing a diet to the user for weight loss,suggesting the user visits a psychiatrist (on a sound assumption thatthe user is depressed), suggesting the user sees a nutritionist and/orpersonal trainer (on the side assumption that the user is overweight),or recommend that the user have his blood sugar checked for diabetes andor high cholesterol. Such actions can follow when the user scores poorlyon knowledge in topics that have synergy or relation to one another whenconsidered for physiological or mental health.

FIG. 7C illustrates an example method for choosing questions to provideto a user based on data retrieved from activity monitoring devices. Indescribing an example method of FIG. 7A, reference may be made toelements of FIG. 1 , FIG. 2 or FIG. 3 for purpose of illustrating asuitable component for performing a step or sub-step being described.

With reference to an example of FIG. 7A, a set of questions can bestored, were at least some of the questions are based on assertions thatare core relative to health (750). For example, questions can be storedin the question library 152, after being processed using a system suchas described with an example of FIG. 2 . The stored questions caninclude both (i) health correlative questions, which are used indetermining a health outcome score or determination for the user (752);and (ii) non-health correlative questions. While the latter questionsmay pertain to health, those questions have either not been determinedto be correlative for health, or those questions have little relevanceto awareness for health, and thus correlative to actual human health(754). As mentioned with other examples, a gaming environment can beimplemented in which the questions are provided as trivia, so that usersreceive entertainment benefit from participating in answering questions.

Still further, as described with other examples, the health correlativequestions can be processed to determine a health correlative parameter(755). For example, question analysis sub-system 200 can be used todetermine a health correlative parameter 151 a for a given question.Still further, as described with other examples, the health correlativeparameter can be based on persons in the control population who haveknowledge (or knowledge deficit thereof) of an assertion underlying theparticular question (760).

In some aspects, in order to contextually choose questions, questionselection 120 can retrieve data generated by activity monitoring devices191 (762). This can include direct indicators of health such as heartrate, blood sugar, and cholesterol levels (763) as well as informationregarding exercise such as steps taken per day, calories burned, andaverage activity levels (764). These devices typically include sensorssuch as accelerometers, a gyroscope, compass, GPS, and a light sensor,among others, that can be used to calculate certain health parameterslike quality of sleep and distance traveled per day (765). In addition,GPS and clock data can be combined with other health and fitness data inorder to determine a user's schedule and where the user is located(766).

After retrieving the activity monitoring device data, question selection120 can choose questions taking into account the data (768). Forexample, if health data shows that a user has high blood pressure,questions relating to how to lower blood pressure can be chosen. If theuser is shown to have poor sleep quality, questions about tips to getbetter sleep can be chosen. If the user has just finished a workout,questions about post-workout recovery can be chosen. If a user isdetermined to be a new runner, questions about basic running knowledgecan be chosen, whereas if a user is an advanced runner, more advancedquestions can be chosen instead.

Location data and time data can also be used to interpret a user'sschedule and choose appropriate schedule-related questions. For example,if the data show that a user commutes via a long subway ride everyweekday, questions about exercise ideas for long commuters can be shown.If a user is detected in a restaurant, questions regarding healthy foodchoices can be shown, and if a user is in a grocery store, questionsabout vegetables, organic food, and nutrition can be shown.

In order to encourage participation and development of accurate healthoutcome scores and determinations, a gaming environment can beestablished in which users are asked questions in a competitive orsemi-competitive context (720). An example of a gaming environment isshown with environments depicted through interfaces of FIG. 8A through8H.

Example Interfaces

FIG. 8A through 8H illustrate example interfaces for use with one ormore embodiments described herein. Interfaces such as described withFIGS. 8A through 8H can be implemented using, for example, a system suchas described with an example of FIG. 1 . Accordingly, reference may bemade to elements of FIG. 1 for purpose of illustrating suitablecomponents for implementing an interface as described.

In FIG. 8A, in interface 800 provides a topical selection 804 for a user(e.g., nutrition). The interface 800 can be displayed with informationfrom the user's profile 138, such as their game score 802 (e.g.,provided as game data 119 of the user's profile, in an example of FIG. 1) and badges or certifications 805.

The panel 810 of FIG. 8B illustrates a question 812, in the form oftrivia. A set of answers 814 can be provided to the user, from which theuser can make selection of in order to affect his or her score.

FIG. 8C illustrates a panel 820 that provides feedback 825 to the useras to the correctness of the answer, as well as supplemental informationregarding the correct answer and/or assertion underlying the question.In FIG. 8D, once the user provides the answer, the user can be providedan additional panel 830, displaying the underlying assertion 832 behindthe question. Other information, such as the percentage of individualswho answer the question correctly can be displayed to the user. Thisfeature 834 can also reflect the difficulty level of the question.

FIG. 8E illustrates a panel 840 on which a menu of options is provided.The user can select from the menu of options. As shown, thefunctionality provided includes gaming (e.g., leader board) andcommunity interaction (e.g., discussions), in a gaming and socialenvironment such as described with an example of FIG. 1 . Additionally,the menu of options can include a health report feature 842 that candisplay, for example, recommendations as determined from an example ofFIG. 7 .

FIG. 8F illustrates a panel 850 that provides a gaming summary for theuser, displaying the user's overall score 852, as well as badges arehonors marking 854 achievements in the number of questions the useranswered, etc.

FIG. 8G illustrates a panel 860 on which a leaderboard 862 is provided.The leaderboard can be topic specific and/or categorized by user level.

FIG. 8H illustrates the panel 870 for enabling social interaction,gaming and knowledge base forums through a system such as described withan example of FIG. 1 . Among other social interaction functions, one ormore knowledge base “twins” can be identified to the user. The twins cancorrespond to an individual who closely shares one or more of (i)knowledge profile about health, or certain topics of health with theuser, and/or (ii) similar or same health outcome values ordeterminations. As an addition or variation, the twin can also includesimilar demographic profile, such as having the same gender, age and/orrace. Identify twins can be shown to each other as a mechanism forbuilding social interaction and shared experiences, particularly as todistributing health-based knowledge, information and services.

Computer System

One or more embodiments described herein provide that methods,techniques and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmaticallymeans through the use of code, or computer-executable instructions. Aprogrammatically performed step may or may not be automatic.

One or more embodiments described herein may be implemented usingprogrammatic modules or components. A programmatic module or componentmay include a program, a subroutine, a portion of a program, or asoftware or a hardware component capable of performing one or morestated tasks or functions. As used herein, a module or component canexist on a hardware component independently of other modules orcomponents. Alternatively, a module or component can be a shared elementor process of other modules, programs or machines.

Furthermore, one or more embodiments described herein may be implementedthrough instructions that are executable by one or more processors.These instructions may be carried on a computer-readable medium.Machines shown or described with figures below provide examples ofprocessing resources and computer-readable mediums on which instructionsfor implementing embodiments of the invention can be carried and/orexecuted. In particular, the numerous machines shown with embodiments ofthe invention include processor(s) and various forms of memory forholding data and instructions. Examples of computer-readable mediumsinclude permanent memory storage devices, such as hard drives onpersonal computers or servers. Other examples of computer storagemediums include portable storage units, such as CD or DVD units, flashor solid state memory (such as carried on many cell phones and consumerelectronic devices) and magnetic memory. Computers, terminals, networkenabled devices (e.g., mobile devices such as cell phones) are allexamples of machines and devices that utilize processors, memory, andinstructions stored on computer-readable mediums. Additionally,embodiments may be implemented in the form of computer-programs, or acomputer usable carrier medium capable of carrying such a program.

FIG. 9 is a block diagram that illustrates a computer system upon whichembodiments described herein may be implemented. For example, in thecontext of FIG. 1 , FIG. 2 , FIG. 6B and FIG. 7B, a network service orsystem can be implemented using one or more computer systems such asdescribed by FIG. 9 . Still further, methods such as described with FIG.4 , FIG. 5 , FIG. 6A and FIG. 7A can be implemented using a computersystem such as described with an example of FIG. 9 .

In an embodiment, computer system 900 includes processor 904, memory 906(including non-transitory memory), storage device, and communicationinterface 918. Computer system 900 includes at least one processor 904for processing information. Computer system 900 also includes a memory906, such as a random access memory (RAM) or other dynamic storagedevice, for storing information and instructions to be executed byprocessor 904. The memory 906 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 904. Computer system 900 mayalso include a read only memory (ROM) or other static storage device forstoring static information and instructions for processor 904. Thecommunication interface 918 may enable the computer system 900 tocommunicate with one or more networks through use of the network link920 (wireless or wireline).

In one implementation, memory 906 may store instructions forimplementing functionality such as described with example systems orsub-systems of FIG. 1 , FIG. 2 , FIG. 6B or FIG. 7B, or implementedthrough example methods such as described with FIG. 4 , FIG. 5 , FIG. 6Aor FIG. 7A. Likewise, the processor 904 may execute the instructions inproviding functionality as described with example systems or sub-systemsof FIG. 1 , FIG. 2 , FIG. 6B or FIG. 7B, or performing operations asdescribed with example methods of FIG. 4 , FIG. 5 , FIG. 6A or FIG. 7A.

Embodiments described herein are related to the use of computer system900 for implementing functionality as described herein. The memory 906,for example, can store a question library 931 (see, e.g., also questionlibrary 152 of FIG. 1 ), including values for health correlativeparameters 933 (see e.g., also health correlative parameters 151 a ofFIG. 1 ) of the questions. The memory 906 can also store instructions941 for determining a health score, in order to determine one or morecorrelative health parameters for a user, in connection with the user'sparticipation of responding to questions in an interactive community orgame environment. According to one embodiment, functionality such asdescribed herein can be performed by computer system 900 in response toprocessor 904 executing one or more sequences of one or moreinstructions contained in the memory 906. Such instructions may be readinto memory 906 from another machine-readable medium, such as through anon-transitory storage device. Execution of the sequences ofinstructions contained in memory 906 causes processor 904 to perform theprocess steps described herein. In alternative embodiments, hard-wiredcircuitry may be used in place of or in combination with softwareinstructions to implement embodiments described herein. Thus,embodiments described are not limited to any specific combination ofhardware circuitry and software.

Mortality System Overview

FIG. 10 illustrates a real-world mortality information system, accordingto an embodiment. In particular, FIG. 10 illustrates a real-worldmortality information system 1000 for extracting real-world data aboutmortality outcomes for the purpose of determining whether knowledge ofunderlying assertions can be correlated to mortality rate. According tosome embodiments, individual questions, or alternatively groups ofquestions, can be correlated to a quantifiable metric that statisticallyrelates a user's knowledge (or lack of knowledge) for an underlyingassertion to a likelihood of a mortality outcome. Some embodiments ofthe system 1000 can be implemented as a sub-system of a health/mortalitypredictive system 100, such as shown with an example of FIG. 1 .Determinations such as described with various examples may includedetermining health, mental health, mortality, morbidity, or underwritingclass for health/life insurance.

In more detail, system 1000 includes one or more third party interfaces1010 that can indicate whether an individual has passed away. The thirdparty interfaces 1010, for example, can include interfaces to socialmedia sites (e.g., FACEBOOK, TWITTER, LINKEDIN, INSTAGRAM, etc.) and/orobituary services, which can provide information about individuals of auser base, including whether users are deceased. Social media sites may,for example, carry content marking the death of a user. Such content mayinclude photographs and/or text posted by friends and family. The socialmedia accounts of users may be accessed by their content. As describedwith numerous examples, in subsequent months and years, the accounts ofusers with such sites may be accessed to determine (or verifydetermination made through a separate source) those users who havedeceased. In some variations, the social media accounts of friends,family members, and/or employers for individual users may also beutilized to determine (or verify) when a user is deceased.

The system 1000 can include a content analysis system 1020 that includesfunctionality for receiving real-world user data 1011 from third partyinterfaces 1010. The third-party interfaces 1010 may include socialmedia sites and/or obituaries services. The real-world user data 1011can include social network content and/or obituary content.

In one implementation, the content analysis system 1020 utilizesanalysis rules 1022 to generate a determination of a real-worldmortality outcome (e.g., whether the user is deceased, living, and/orunknown) for an individual. The analysis rules 1022 can be implementedas a formula or model, and can image analysis 1030 and/or text analysislogic 1040.

In some embodiments, the image analysis 1030 and/or text analysis logic1040 data can operate on respective image and text content to identifyhints, clues or explicit evidence of a user's mortality outcome. Forexample, text analysis logic 1040 may parse text content from a socialnetwork account to identify tribute markers and/or keywords 1032.Likewise, image analysis 1030 can recognize text in images in order toidentify tribute markers/keywords 1032. Thus, the tributemarkers/keywords 1032 may be markers identified through image and/orword recognition. By way of example, tribute markers may include “wewill miss you,” “rest in peace,” “our condolences,” “sorry for yourloss,” “in a better place,” “everything happens for a reason,” “will bein our thoughts and prayers,” etc. Markers through image recognition canalso be extracted from photographs, such as photographs of grave markersor tombstones.

In some variations, the social networking content of relatives, friendsor employers may also be analyzed to confirm or weight a determinationof mortality outcome. For example, links to the social networking pageof a given user may be analyzed to determine an account of a spouse orclose relative. The social networking account of the relative can thenbe analyzed for markers that indicate the loss of a loved one. Themarkers may be specific to the relationship between the relative and theperson for whom the mortality determination is being made.

A sentiment analysis component 1034 can also analyze social networkcontent associated with a user account to determine an overall sentiment1035 for the social network content in a given time frame. The sentimentanalysis component 1034 may utilize an output of image analysis 1030and/or text analysis 1040 (e.g., tribute markers/keywords 1032). As analternative or addition, the sentiment analysis component 1034 mayprocess social network content directly from the user's account. Anoutput of the sentiment analysis component 1034 may include a sentiment1035. The sentiment 1035 may represent one of multiple possiblesentiment values, such as (i) sad or not sad, (ii) sad, happy, neutralor (iii) sad, happy, angry, neutral.

Mortality determination logic 1046 may receive input from 1020, anddetermine a mortality output 1049. The mortality output 1049 canidentify a user (e.g., by name, by moniker, hashed identifier, etc.) andassociate the user with a mortality determination. The mortalitydetermination can correspond to, for example, one of alive, deceased,unknown. In variations, the mortality determination can also beassociated with a confidence score, which indicates a certainty of thedetermination. For example, if the confidence score is above athreshold, the mortality determination may indicate that the user isdeceased. However, if the confidence score is below the threshold, themortality determination may indicate that the user is likely deceased,or alternatively, unknown as to alive or deceased.

According to examples, the mortality determination logic 1046 employsprogrammatic methodologies in order to make the mortality determinationfor given individual automatically, and without input from a human. Insome examples, system 1000 operates in an anonymized manner, so thatinput and output from system 1000 is not accessible to humans. Forexample, real-world user data 1011 may be encrypted, and the associationbetween user and mortality determination may be in the form of aprotected tuple a data set. The programmatic methodologies can include,for example neural network or random forest algorithms, which canassociate weights and confidence values to markers/keywords 1032. Insome examples, the mortality determination logic 1046 also obtainscontextual data relating to markers/keywords 1032. The contextual datacan for example, identify relative placement of keywords in a phrase(e.g., words towards the beginning of a sentence versus in the middle ofa paragraph), placement of markers/tributes 1032 in captions of images,prominence of markers/tributes 1032, etc.

Additionally, in some examples, sentiment 1035 can be used to weightmarkers/keywords 1032 for or against a particular determination. Forexample, the marker/tribute 1032 for a particular user may include akeyword that is strongly associated with a mortality determination ofdeceased, but the particular marker may be devalued (e.g., associatedwith a low confidence score) because a sentiment 1035 of posts andcomments related to the text indicate a relatively happy mood.

In some examples, when the mortality determination logic 1046 determinesthat an individual is deceased, the name of the deceased individual canbe generated and sent to a name comparison component 1050, whichcompares the deceased's name to names within a user name database 1060.The user name database 1060 may, for example, include names ofindividuals within a user population, or alternatively, within a controlpopulation of the user base. The name of the deceased individual may beincluded as real-world information 1070 that is output once a match to aname in the user name database 1060 has been established. The real-worldinformation 1070 can be compared to expected mortality outcomes in aphysiological health, mental health, mortality, morbidity, and/orunderwriting predictive system 100 (such as shown with an example ofFIG. 1 ), where the comparison between actual mortality outcomes andexpected mortality outcomes within the control group can be included indetermining how knowledge of underlying assertions is correlated withmortality rate.

In some variations, the content analysis system 1020 may receive usernames within the user name database 1060 and then analyze social networkcontent from third party interfaces 1010 for social network accountsassociated with those user names. For example, the content analysissystem 1020 can receive a user name from the user name database 1060through the name compare component 1050. The third party interfaces 1010can be searched with each received user name to verify whether theindividual associated with each user name is deceased. This can be doneon a continuous basis.

According to some examples, when individuals in the user name database1060 are determined to be deceased, they can be added to the controlgroup for determining correlative mortality parameter values. In someexamples, the individuals are added to the control group if certainconditions are satisfied, such as age (e.g., only individuals above 50are added to the control group), cause of death (e.g., accidental ornon-natural cause of death is not considered).

In some variations, one or more additional verification processes can beperformed to verify an alternate determination that an individual of acontrol group (or candidate thereof) is deceased. For example, system100 may identify past and current users who have deceased from a givensource, such as third-party obituary services. In such examples, aseparate verification process (or set of processes) can be implementedto verify a past or current user that is determined to be deceasedthrough the alternate source. In some examples, a programmatic processmay be implemented to confirm or otherwise verify that an individual isdeceased.

In one implementation, the programmatic verification process may includeanalyzing content of the social networking page of the users who havebeen identified as deceased through an alternative or independentsource. For such users, the verification process can include scanningcontent of social networking accounts of users who have been identifiedas deceased in order to confirm the user is deceased. As described withother examples, the analysis can include parsing text and analyzingimages for markers that are typical for deceased persons.

In some variations, social networking accounts of family members forsuch users can be identified programmatically, through, for example,links provided on a user's social networking page. The accounts of thefamily members may also be analyzed to further verify the determinationthat the original user is deceased. For example, the social networkingpage of a spouse can be analyzed for markers that indicate a loss of theindividual's spouse. The markers may be specific to the relationshipbetween the family member and the original user who is thought to bedeceased.

Lifestyle Categorization Determinations

FIG. 11 illustrates a system to categorize a lifestyle of a user,according to an embodiment. In some examples, a lifestyle determinationsystem 1100 extracts and analyzes social media content about a user inorder to determine one or more lifestyle categorizations of the user.The system 1100 may be implemented through one or more computers, suchas described with, for example, FIG. 9 . For example, the system 1100may be implemented through use of a server, or set of servers, whichprogrammatically interface with social networking sites in order todetermine lifestyle categorization, as described with examples providedbelow.

According to some embodiments, individual questions, or alternativelygroups or series of questions, can be correlated to a quantifiablemetric that statistically relates a user's knowledge (or alternativelylack of knowledge) of an underlying assertion to one or morecharacteristics of the user. The one or more characteristics of the usermay be related to the user's physiological health, mental health,mortality rate, morbidity rate, and/or underwriting class can beassociated with one or more related attributes that can be verifiedthrough social media content. Some embodiments of the lifestyledetermination system 1100, for example, can be implemented as asub-system of a system 100, such as shown in FIG. 1 .

In an example of FIG. 11 , the system 1100 may include a usercategorization system 1102 and a social network content analysis(“SNCA”) component 1108. The SNCA component 1108 analyzes social networkcontent of individual users to determine characteristics or traits ofthe user, from text and/or image content, as well as metadata andaffiliations or social connections.

The SNCA component 1108 may utilize programmatic interfaces to socialnetworking services, in order to retrieve content from a specific user'saccount. In more detail, SNCA component 1108 may include functionality(“social network connectors 1118”) for retrieving or otherwise obtainingsocial network content of a user from a particular source. The socialnetwork connectors 1118 can, for example, interface with specific useraccounts on social media sites such as FACEBOOK, TWITTER, LINKEDIN,INSTAGRAM, etc., to retrieve text and images from the user's account. Insome variations, the SNCA component 1108 may utilize social networksconnectors 1118 to retrieve social network content from accounts whichare linked to the user, for individuals whom are identified as thespouse, sibling, parent, child, close friend or co-worker.

In some implementations, the social network content of a user can beanalyzed to determine a set of attributes 1106 from which a lifestylecategorization may be inferred. The SNCA component 1108 may include animage analysis component 1112, a text analysis component 1114, and anaffiliation analysis component 1116. The SNCA component 1108 may use theimage analysis logic 1112 to perform image analysis of objects, text orpersons, in order to determine markers of a particular lifestylecategory. For example, the image analysis logic 1112 may be analyzed todetermine whether the user participates in sporting events, attendsbarbecues, or is a smoker. The text analysis logic 1114 may parsecaptions, comments, or postings of the user to determine events oractivities which the user participated in, as well as hobbies orinterests of the user. In some implementations, the text analysis logic1114 may access a list or library of keywords in order to determinewhether the keywords exist in the social network content of a givenuser. As an addition or variation, the text analysis logic 1114 mayidentify contextual data about textual content, including placement ofindividual words relative to, for example, a beginning of an entry or toother words. Still further, in some variations, the text analysis logic1114 may perform sentiment analysis on individual posts or entries todetermine a user's sentiment about a particular event or caption.

The affiliation logic 1116 can identify social connections of the user,and process content or known information about the connections in orderto determine interests, activities, events or other information about auser. The relevance of identified affiliations may be based onassumptions. In some examples, affiliations may be used as a form ofverification. For example, an assumption may exist that vegans andsmokers tend to have spouses who are also vegans or smokers. The SNCAcomponent 1108 may thus identify such connections using the affiliationlogic 1116, and then process the social network content of suchconnections using the image analysis logic 1112 and the text analysislogic 1114.

In some embodiments, the image analysis logic 1112, the text analysislogic 1114, and/or the affiliation logic 1116 data can be analyzed forrespective attributes 1106 of a predefined lifestyle category. Theattributes 1106 can be determined from social network content 1101through image and textual analysis. Additionally and/or alternatively,content can be associated with connections of the user in a particularsocial network forum. As output, the SNCA component 1108 may determineone or multiple attributes 1106, each of which may relate to a lifestylecategory.

In some variations, the user's commentary or activity can also bedetected and analyzed. For example, the users “likes” or comments can bedetected, and a subject of the activity may be extracted from thecontent and analyzed for text and image content.

The SNCA component 1108 may determine individual attributes 1106 alongwith a score which indicates a confidence that the attribute is that ofthe user. For example, the SNCA component 1108 may determine as anattribute, that the user is a turkey eater by analyzing Thanksgivingpictures from the user's social network account. The determination thatthe user is a turkey eater can be relevant to whether the user is avegan. The attribute 1106 for Thanksgiving turkey eater can be provideda score that measures a confidence related to one or more of thefollowing: (i) the content analyzed in fact showed a turkey as part of aThanksgiving feast (e.g., image of cooked turkey), (ii) the userparticipated in that particular Thanksgiving feast (e.g., “lovely dinnerat our in-laws”), and/or (iii) the user ate turkey on that event (e.g.,text content “Turkey was delicious!”).

As another example, the SNCA component 1108 may identify attributes 1106which have strong associations with a particular lifestyle category. Forexample, the affiliation logic 1116 may identify the presence of a linkto a cigar club, which would provide strong indication that the personsmokes cigars. At least one of the attributes 1106 which is output bythe SNCA component 1108 may identify as cigar smoker.

Similarly, the image analysis logic 1112 may be trained to detectcigarettes (e.g., orange tips and smoke) in the user's images, and thenperform image recognition to identify the user's face in relation to thecigarette. In such an example, the SNCA component 1108 may output anattribute 1106 of cigarette smoker. A confidence score may reflect acertainty to which a cigarette was detected in the social content of theuser, as well as a certainty that the user was the one smoking thecigarette.

As another example, the SNCA component 1108 may be triggered to retrievecontent from a spouse of a user in order to determine second orderattributes 1107, which may be probative of a particular lifestylecategorization. For example, an assumption may be implemented by theuser categorization system 1102, such as an assumption that vegans havespouses that are also vegans. Still further, the SNCA component 1108 mayanalyze affiliations to identify groups or organizations which the usermay support. For example, the user categorization system 1102 may alsoimplement the assumption that an SPCA supporter is more likely to be avegan.

In some embodiments, the attributes 1106 which are determined for theuser can include both positive and negative attributes, where positiveattributes correlate positively with the categorization and negativeattributes inversely correlate with the categorization. For example, ifthe user is categorized as “vegan,” an affinity for barbecue (e.g., theuser hosts a barbecue, as determined from text and image content) wouldbe a negative attribute that would cause the system 1100 to determinethe categorization may not be accurate for the user.

Still further, the social network content 1101 of users can be analyzedfor markers and/or keywords that correlate to attributes 1106.Additionally, connections and affiliations, such as of related persons(e.g., spouse, sibling, child, parent, close friend, employer, supportedorganization) can be identified for relevance. In some examples, socialnetwork content from such connections may be retrieved and analyzed forprobative attributes 1106. For example, a user categorized as “vegan”may have a spouse identified through FACEBOOK. In that case, the system1100 can search for the spouse's name and, if there is a match, bettercorrelate the user with the “vegan” category (e.g., by weighting thecategory more heavily).

The user categorization system 1102 may implement one or more rules ormodels (e.g., neural network, random forest) to determine one or morelifestyle categories for a user, based at least in part on theattributes 1106 determined from processing the user's social networkingcontent. By way of example, the lifestyle categories can include (i)whether the user is a vegetarian, (ii) whether the user is a smoker,(ii) whether the user is a cigar smoker, (iv) whether the user sits alot during the workday (e.g., based on images from work), (v) whetherthe user participates in athletic events, (vi) whether the user prepareshis own food or eats out. In variations, more refined categorizationsmay be utilized, such as dietary habits (e.g., Mediterranean, vegan,vegetarian, pescatarian, raw food, paleo, etc.), exercise habits (e.g.,athlete), and/or demographic information (e.g., wealth, class status,education level, age, gender, geographic location, families and livingarrangements, etc.). Each lifestyle category 1111 can include a categorydefinition.

In some examples, the category definitions may include a set ofattributes which individually carry a different weight relative to otherattributes in regards to determining whether a particular lifestylecategory is applicable to a user based on their social network content.According to some examples, the category definitions for individualcategories include (i) one or more attributes which necessarily indicatethe user has a particular lifestyle category (e.g., content showing theuser eating meat means he is not a vegan, and non-vegan lifestylecategory), (ii) one or more attributes which strongly indicate the useris of a particular lifestyle category (e.g., a single picture of theuser near a cigarette is a strong indicator, but not conclusive that theuser is a smoker), and/or (iii) one or more attributes which moderatelyindicative of a lifestyle category (e.g., a user posting about runs ismoderately indicative of an athletic lifestyle). The user categorizationsystem 1102 may implement a model or scoring algorithm to determine,from attributes 1106, which of multiple possible lifestyle categoriesthe user belongs in.

As an addition or variation, the user categorization system 1102 maydetermine one or more user parameters that correlate to the determinedlifestyle categorization 1124. In some examples, the user categorizationsystem 1102 can associate a user categorization score associated withthe lifestyle categorizations 1124 of the user. The user categorizationscore may be a percentage or weight correlated with the likelihood ofthe user falling within the categorization. In variations, the usercategorization score may reflect a magnitude by which a person can beviewed as having a particular lifestyle (e.g., light smoker versus heavysmoker; pure vegan or vegan with exceptions, etc.).

In some implementations, the lifestyle categorizations 1124 can be usedto configure implementation and/or use of system 100 for a given user.For example, the lifestyle categorizations 1124 can be stored with auser profile, and the lifestyle categorizations 1124 can be used todetermine which questions to ask the user. Alternatively, the lifestylecategorizations 1124 can be used to weight the health outcome score 165a and/or mortality outcome parameter 165 b determined from the system100.

As an addition or variation, the lifestyle categorizations 1124 can beutilized as input in determining, for example, a person's eligibility orpricing for a product or service that can be valued on, for example, theuser's well-being and/or longevity. By way of example, the lifestylecategorizations 1124 can be used to reduce the premium or deductible aperson pays for health or life insurance. Still further, the lifestylecategorizations 1124 may be used to determine an individual'seligibility for a product or service.

In some variations, the lifestyle categorizations 1124 is used incombination with one or more other inputs in order to determineeligibility or pricing for products or services such as health and lifeinsurance. For example, the lifestyle categorizations 1124 may be usedin combination with outputs of system 100 in order to determine pricingand/or eligibility for insurance products.

In some examples, the lifestyle categorizations 1124 can be predictivewithout input from the user. The categorizations 1124 can be associatedwith characteristics of the user that can be quantifiable such that thecharacteristics (and, accordingly, categorizations) can be verifiablefrom independent sources, such as subsequent input from the user or fromdeterminations of system 100 (e.g., after user answers sufficientquestions to develop profile). In some examples, the lifestylecategorizations 1124 can be verified by independent sources, and thenused to train one or more models of the user categorization system 1102.

In some embodiments, the lifestyle categorizations 1124 can be used toverify or refine categorizations of a user based on the user's knowledgeof assertions, as determined from answering questions selected from thesystem 100. For example, the system 100 can generate fact-basedquestions on various topics of health for the purposes of (i) obtainingresponses from users, and (ii) correlating all or some of thoseresponses to physiological health, mental health, mortality rate,morbidity rate, and/or underwriting class (for health or life insurance)predictions. Accordingly, a user may make an assertion related to (e.g.,answer) one or more fact based questions, and from the user'sassertions, the user's independent knowledge of the assertion can bedetermined. From this, the user categorization system 1102 can makepredictions about the user's attributes and, accordingly, categorize theuser on that basis. In some embodiments, correlating responses tocategory predictions may be done through a correlative model or formulathat is developed using a control population, such that the user'sindependent knowledge can be compared to the control population'sindependent knowledge.

FIG. 12 illustrates an automated method for utilizing information abouta user. A method such as described with an example of FIG. 12 may beimplemented using, for example, a system such as described with anexample of FIG. 1 or FIG. 11 . Accordingly, reference may be made toelements of other examples for purpose of illustrating a suitablecomponent for performing a step or sub-step being described.

With reference to FIG. 12 , the system 1100 may obtain social networkcontent for one or more users of a user base (1210). The user base maycorrespond to, for example, users of system 100, who may answerquestions and have their knowledge evaluated to correlate theirknowledge with health and mortality outcomes. Accordingly, the socialnetwork content may be determined using, for example, social networkconnectors 1118, which automatically retrieve content for individualusers, or for a group of users in bulk.

SNCA component 1108 may analyze the social network content to determineone or more attributes for each user (1220). The text analysis logic1114 of the SNCA component 1108 can include text analysis, whichanalyzes keywords, placement of keywords, and contextual data of keywords and other text on a user's social networking page (1222). The keywords can be filtered or correlated to a set of attributes, which arepredetermined to a definition of a lifestyle category.

As an addition or alternative, the SNCA component 1108 may include theimage analysis logic 1112, which can execute to recognize predeterminedobjects which are deemed markers of a particular lifestyle category(1224). For example, the system 1100 may use the image analysis logic1112 to recognize the tips of lit cigarettes, in order to determine anattribute for the lifestyle category of a smoker. Similarly, the system1100 may use the image analysis logic 1112 to recognize cooked meat,such as a turkey or burger, in order to determine an attribute fornon-vegan.

Still further, the affiliation logic 1116 may identify affiliations,such as in the form of social connections with other users orassociations (1226). Some affiliations to associations, for example, maybe identified as an attribute which are relevant to a particularlifestyle (e.g., SPCA, for vegan). The affiliation logic 1116 may alsoidentify social network content of other users to obtain and analyze.

In determining the attributes, the system 1100 may also determine anassociated confidence score, which can relate to the confidence of thedetermination (1228). As an addition or alternative, the confidencescore can measure the likely link between the user and the determinedattribute. For example, a weak link (e.g., picture of turkey without theuser at the table) may be indicated by a lower score than a strong link(e.g., user at table with turkey).

According to some examples, the system 1100 may determine a lifestylecategory for the user based at least in part on the determinedattributes (1230). The system 1100 may employ, for example, the usercategorization system 1102, which can implement one or more models,rules or other logic to predict the lifestyle category for the user. Thepredicted lifestyle category may be associated with a confidence score,indicating the certainty of the determination.

FIG. 13 illustrates a health determination system for determininghealth-based products and services, according to one or moreembodiments. According to some examples, a health determination system1300 includes one or more health determination components whichindividually or collectively predict a health-related aspect of theuser. The health-related aspect may correspond to, for example, acharacterization (e.g., quantitative or qualitative) of the user'soverall health. As an addition or variation, the determinedhealth-related aspect may be predictive of a relative health of theuser, based on a demographic parameter such as age and race of the user.Still further, the determined health-related aspect may be predictive oflongevity, mortality or morbidity.

According to one implementation, a knowledge determination component1310 provides an output that correlates a user's knowledge ofhealth-related assertions to a health or mortality outcome. In someexamples, the knowledge determination component 1310 may be implementedusing a system such as described with FIG. 1 , and an output of theknowledge determination component 1310 may correspond to a knowledgecorrelative determination 1336. The knowledge correlative determination1336 may represent a value that correlates a knowledge of the user abouta health topic to a predictive health outcome. In particular, theknowledge correlative determination 1336 may correspond to a score orparametric value that for example, is predictive of one or more of ahealth outcome of the user, a health state of the user, and/or amortality of the user. The knowledge correlative determination 1336 maybe provided as input to the user health services 1340, to select one ormore health products or services.

A lifestyle categorization component 1320 may be implemented toprogrammatically determine a lifestyle category of a user (shown as“lifestyle determination 1334”). According to some examples, thelifestyle categorization component 1320 may receive social networkcontent 1312 from one or more social network sites 1308. As describedwith an example of FIG. 11 , the lifestyle categorization component 1320may analyze text and image content, as well as affiliations of a givenuser's social network content, in order to make one or more lifestyledeterminations 1334. The lifestyle determinations 1334 may identifycategories, such as a dietary lifestyle (e.g., vegan) or a smokerlifestyle. Examples of specific lifestyle determinations 1334 include aclassification of weightlifter, endurance athlete, a dieter, a personwho practices preventative care, a triathlete or a person who has a lowglycemic diet.

As described with other examples, the lifestyle determinations may bedetermined as confidence scores, using, for example, random forest orneural network algorithms. The algorithms may be trained off of usersself reported habits, and compared to results determined from analyzingposts of individual users.

A user health model 1330 may predict a health profile of a user based oninput that includes one or more of (i) knowledge correlationdetermination 1336 of the knowledge determination component 1310, and/or(ii) lifestyle determinations 1334 as determined from the lifestylecategorization component 1320. As an addition or alternative, the healthdetermination system 1300 may utilize user biological input 1302, whichcan include information such as hip-to-waist ratio, triglyceride levelsof the user, the user weight, body mass index (“BMI”) or otherinformation. As another addition or variation, the user health model1330 may use social network content 1312 as input. The user health model1330 may implement any one or more algorithms, to determine healthprofiles 1332 for individual users. In some examples, the healthprofiles 1332 may include classifications of the user's overall healthor mortality. The classifications may be quantitative. Additionally, theclassifications may be provided with a confidence score, indicating, forexample, a probability that the classification for the user is accurate.

The user health services 1340 may include functionality to select ahealth service or product for the user. The health service or productmay be of a type in which a price or other material aspect is dependenton a health of the user. The user health services 1340 may use inputs,corresponding to, for example, one or more of the health profile 1332,lifestyle determinations 1334, and/or the knowledge correlativedeterminations 1336. The user health services 1340 may includefunctionality 1342 to select a health service or product for the user.The health service or product may be of a type in which a price or othermaterial aspect is dependent on a health of the user. Based on one ormore of the inputs, the user health services 1340 may determine if auser may qualify for a health product or service, which includes variousforms of life and health insurance. As an alternative or variation, theuser health services 1340 may include functionality 1344 for determine apricing for the product or service. The pricing may correspond to, forexample, a price for a premium or deductible (e.g., or discount forsame).

According to some examples, the user health services 1340 may determinea premium or deductible for a health or life insurance based on apredicted life expectancy of the user. In variations, the user healthservices 1340 may determine the premium or life expectancy based oninformation about the user and a class or sub-class of the user. Theinformation about the user may be determined by one or more of (i) aknowledge correlative determination 1336 of the user, (ii) thebiological input 1302 provided by the user. Thus, the user healthservices 1340 may utilize both personal information and informationabout a class or sub-class of the user.

What is claimed is:
 1. A computing system implementing morbidityprediction for a health service, the computing system comprising: anetwork communication interface to communicate, over one or morewireless networks, with computing devices of users of the healthservice; one or more processors; and a memory storing instructions that,when executed by the one or more processors, cause the computing systemto: execute a correlation model to determine a correlation value foreach respective health assertion in a collection of health assertionsbased on (i) answers to the respective health assertion provided byindividuals in a control group, and (ii) known health outcomes of eachindividual in the control group, wherein the correlation value for eachrespective health assertion in the collection corresponds to a set ofhealth correlations between knowledge associated with the respectivehealth assertion and the known health outcomes of the individuals in thecontrol group, wherein the collection of health assertions areconfigured to test general health knowledge of the user of the healthservice and not query user-specific information of the users; generate,over the one or more wireless networks, a health trivia session to bepresented on a computing device of a user, the health trivia sessioncomprising a set of health assertions from the collection of healthassertions; receive, over the one or more wireless networks, acorresponding set of responses to the set of health assertions from thecomputing device of the user; for each response in the corresponding setof responses, determine a correctness for the response, the correctnessindicating whether the user answered a corresponding health assertioncorrectly or incorrectly; based on (i) the correctness of each responsein the corresponding set of responses, and (ii) the correlation value ofeach health assertion in the set of health assertions provided duringthe trivia session, generate a morbidity profile for the user, themorbidity profile corresponding to one or more health risks of the user;based on the morbidity profile of the user, determine an underwritingclass for a health service product for the user; based on theunderwriting class for the health service product for the user,determine a specified price for the health service product; andgenerate, over the one or more wireless networks, a service customerinterface to be displayed on the computing device of the user, theservice customer interface enabling the user to purchase the healthservice product in the specified underwriting class and at the specifiedprice.
 2. The computing system of claim 1, wherein the executedinstructions further cause the computing system to: access, over the oneor more wireless networks, social media content of the user; and basedon the social media content, classify the user into one or morelifestyle categories.
 3. The computing system of claim 2, wherein theexecuted instructions cause the computing system to further generate themorbidity profile for the user based on the one or more lifestylecategories of the user as determined from the social media content. 4.The computing system of claim 1, wherein the executed instructionsfurther cause the computing system to: access, over the one or morewireless networks, social media data associated with one or moreindividuals in the control group; wherein the executed instructionscause the computing system to determine the known health outcomes of theone or more individuals in the control group based, at least in part, onthe social media data.
 5. The computing system of claim 4, wherein theknown health outcomes of the one or more individuals in the controlgroup comprise morbidity outcomes.
 6. The computing system of claim 1,wherein the underwriting class corresponds to one of a premium or adiscount for the health service product.
 7. The computing system ofclaim 1, wherein the health service product comprises a life insuranceor a health insurance product.
 8. The computing system of claim 1,wherein the set of health assertions for the health trivia sessioncomprise multiple choice health assertions or questions.
 9. Anon-transitory computer readable medium storing instructions that, whenexecuted by one or more processors of a computing system, cause thecomputing system to: communicate, over one or more wireless networks,with computing devices of users of a health service; execute acorrelation model to determine a correlation value for each respectivehealth assertion in a collection of health assertions based on (i)answers to the respective health assertion provided by individuals in acontrol group, and (ii) known health outcomes of each individual in thecontrol group, wherein the correlation value for each respective healthassertion in the collection corresponds to a set of health correlationsbetween knowledge associated with the respective health assertion andthe known health outcomes of the individuals in the control group,wherein the collection of health assertions are configured to testgeneral health knowledge of the user of the health service and not queryuser-specific information of the users; generate, over the one or morewireless networks, a health trivia session to be presented on acomputing device of a user, the health trivia session comprising a setof health assertions from the collection of health assertions; receive,over the one or more wireless networks, a corresponding set of responsesto the set of health assertions from the computing device of the user;for each response in the corresponding set of responses, determine acorrectness for the response, the correctness indicating whether theuser answered a corresponding health assertion correctly or incorrectly;based on (i) the correctness of each response in the corresponding setof responses, and (ii) the correlation value of each health assertion inthe set of health assertions provided during the trivia session,generate a morbidity profile for the user, the morbidity profilecorresponding to one or more health risks of the user; based on themorbidity profile of the user, determine an underwriting class for ahealth service product for the user; based on the underwriting class forthe health service product for the user, determine a specified price forthe health service product; and generate, over the one or more wirelessnetworks, a service customer interface to be displayed on the computingdevice of the user, the service customer interface enabling the user topurchase the health service product in the specified underwriting classand at the specified price.
 10. The non-transitory computer readablemedium of claim 9, wherein the executed instructions further cause thecomputing system to: access, over the one or more wireless networks,social media content of the user; and based on the social media content,classify the user into one or more lifestyle categories.
 11. Thenon-transitory computer readable medium of claim 10, wherein theexecuted instructions cause the computing system to further generate themorbidity profile for the user based on the one or more lifestylecategories of the user as determined from the social media content. 12.The non-transitory computer readable medium of claim 9, wherein theexecuted instructions further cause the computing system to: access,over the one or more wireless networks, social media data associatedwith one or more individuals in the control group; wherein the executedinstructions cause the computing system to determine the known healthoutcomes of the one or more individuals in the control group based, atleast in part, on the social media data.
 13. The non-transitory computerreadable medium of claim 12, wherein the known health outcomes of theone or more individuals in the control group comprise morbidityoutcomes.
 14. The non-transitory computer readable medium of claim 9,wherein the underwriting class corresponds to one of a premium or adiscount for the health service product.
 15. The non-transitory computerreadable medium of claim 9, wherein the health service product comprisesa life insurance or a health insurance product.
 16. The non-transitorycomputer readable medium of claim 9, wherein the set of healthassertions for the health trivia session comprise multiple choice healthassertions or questions.
 17. A computer-executed method of implementingmortality prediction for a health service, the method being performed byone or more processors of a computing system and comprising:communicating, over one or more wireless networks, with computingdevices of users of a health service; executing a correlation model todetermine a correlation value for each respective health assertion in acollection of health assertions based on (i) answers to the respectivehealth assertion provided by individuals in a control group, and (ii)known health outcomes of each individual in the control group, whereinthe correlation value for each respective health assertion in thecollection corresponds to a set of health correlations between knowledgeassociated with the respective health assertion and the known healthoutcomes of the individuals in the control group, wherein the collectionof health assertions are configured to test general health knowledge ofthe user of the health service and not query user-specific informationof the users; generating, over the one or more wireless networks, ahealth trivia session to be presented on a computing device of a user,the health trivia session comprising a set of health assertions from thecollection of health assertions; receiving, over the one or morewireless networks, a corresponding set of responses to the set of healthassertions from the computing device of the user; for each response inthe corresponding set of responses, determining a correctness for theresponse, the correctness indicating whether the user answered acorresponding health assertion correctly or incorrectly; based on (i)the correctness of each response in the corresponding set of responses,and (ii) the correlation value of each health assertion in the set ofhealth assertions provided during the trivia session, generating amorbidity profile for the user, the morbidity profile corresponding toone or more health risks of the user; based on the morbidity profile ofthe user, determining an underwriting class for a health service productfor the user; based on the underwriting class for the health serviceproduct for the user, determining a specified price for the healthservice product; and generating, over the one or more wireless networks,a service customer interface to be displayed on the computing device ofthe user, the service customer interface enabling the user to purchasethe health service product in the specified underwriting class and atthe specified price.
 18. The method of claim 17, further comprising:accessing, over the one or more wireless networks, social media contentof the user; and based on the social media content, classifying the userinto one or more lifestyle categories.
 19. The method of claim 18,wherein the computing system further generates the morbidity profile forthe user based on the one or more lifestyle categories of the user asdetermined from the social media content.
 20. The method of claim 17,further comprising: accessing, over the one or more wireless networks,social media data associated with one or more individuals in the controlgroup; wherein the computing system determines the known health outcomesof the one or more individuals in the control group based, at least inpart, on the social media data.